From 0a89c0ca7e4b3f4ed607435e5c16f4a89ef14f20 Mon Sep 17 00:00:00 2001
From: Michael Zephyr <mzephyr@nvidia.com>
Date: Mon, 9 Jun 2025 16:41:06 -0700
Subject: [PATCH 1/7] Updated the metadata for all models, focused on name,
 description, and task for Model Zoo website improvements.

---
 hf_models/ct_chat/metadata.json                   | 13 +++++++------
 hf_models/exaonepath/metadata.json                | 11 ++++++-----
 hf_models/llama3_vila_m3_13b/metadata.json        | 15 ++++++++-------
 hf_models/llama3_vila_m3_3b/metadata.json         | 13 +++++++------
 hf_models/llama3_vila_m3_8b/metadata.json         | 15 ++++++++-------
 .../configs/metadata.json                         |  8 +++++---
 .../configs/metadata.json                         |  9 +++++----
 .../configs/metadata.json                         |  9 +++++----
 .../brats_mri_segmentation/configs/metadata.json  | 11 ++++++-----
 .../configs/metadata.json                         | 11 ++++++-----
 .../classification_template/configs/metadata.json |  9 +++++----
 .../configs/metadata.json                         |  8 +++++---
 .../configs/metadata.json                         | 14 ++++++++------
 .../configs/metadata.json                         | 12 ++++++------
 .../configs/metadata.json                         |  9 +++++----
 models/maisi_ct_generative/configs/metadata.json  | 15 +++++++++------
 models/mednist_ddpm/configs/metadata.json         |  8 +++++---
 models/mednist_gan/configs/metadata.json          |  7 ++++---
 models/mednist_reg/configs/metadata.json          |  9 +++++----
 models/model_info.json                            |  4 ----
 .../configs/metadata.json                         | 10 ++++++----
 .../configs/metadata.json                         |  9 +++++----
 .../configs/metadata.json                         |  9 +++++----
 .../configs/metadata.json                         | 11 ++++++-----
 .../configs/metadata.json                         |  9 +++++----
 .../configs/metadata.json                         |  9 +++++----
 .../configs/metadata.json                         |  9 +++++----
 models/prostate_mri_anatomy/configs/metadata.json |  9 +++++----
 .../configs/metadata.json                         |  9 +++++----
 .../configs/metadata.json                         |  9 +++++----
 .../segmentation_template/configs/metadata.json   |  9 +++++----
 .../spleen_ct_segmentation/configs/metadata.json  | 11 ++++++-----
 .../configs/metadata.json                         | 13 +++++++------
 .../configs/metadata.json                         | 11 ++++++-----
 models/valve_landmarks/configs/metadata.json      |  9 +++++----
 .../configs/metadata.json                         | 10 ++++++----
 models/vista2d/configs/metadata.json              | 15 ++++++++-------
 models/vista3d/configs/metadata.json              | 10 +++++-----
 .../configs/metadata.json                         |  9 +++++----
 .../configs/metadata.json                         |  9 +++++----
 40 files changed, 225 insertions(+), 184 deletions(-)

diff --git a/hf_models/ct_chat/metadata.json b/hf_models/ct_chat/metadata.json
index 9f9cef0b..fdde5e17 100644
--- a/hf_models/ct_chat/metadata.json
+++ b/hf_models/ct_chat/metadata.json
@@ -1,8 +1,9 @@
 {
     "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_hf_20250321.json",
-    "version": "1.0.0",
+    "version": "1.1.0",
     "changelog": {
-        "1.0.0": "initial release of CT_CHAT model"
+        "1.0.0": "initial release of CT_CHAT model",
+        "1.1.0": "enhanced metadata with improved descriptions, task specification, and intended use documentation"
     },
     "monai_version": "1.4.0",
     "pytorch_version": "2.4.0",
@@ -18,10 +19,10 @@
         "ct_clip": "",
         "ct_chat": ""
     },
-    "name": "CT_CHAT",
-    "task": "Vision-language foundational chat model for 3D chest CT volumes",
-    "description": "CT-CHAT is a multimodal AI assistant designed to enhance the interpretation and diagnostic capabilities of 3D chest CT imaging. Building on the strong foundation of CT-CLIP, it integrates both visual and language processing to handle diverse tasks like visual question answering, report generation, and multiple-choice questions. Trained on over 2.7 million question-answer pairs from CT-RATE, it leverages 3D spatial information, making it superior to 2D-based models.",
-    "authors": "Ibrahim Ethem Hamamci, Sezgin Er, Furkan Almas, et al.",
+    "name": "CT-CHAT",
+    "task": "Vision-Language Chat Model for 3D Chest CT Analysis",
+    "description": "CT-CHAT is a multimodal AI assistant specifically designed for 3D chest CT imaging interpretation and analysis. The model excels at tasks including visual question answering, report generation, and multiple-choice questions, leveraging full 3D spatial information for superior performance compared to 2D-based approaches.",
+    "authors": ["Ibrahim Ethem Hamamci", "Sezgin Er", "Furkan Almas", "et al."],
     "copyright": "Ibrahim Ethem Hamamci and collaborators",
     "data_source": "CT-RATE dataset",
     "data_type": "3D CT volumes and text",
diff --git a/hf_models/exaonepath/metadata.json b/hf_models/exaonepath/metadata.json
index 5c4e92fe..5f6f80e8 100644
--- a/hf_models/exaonepath/metadata.json
+++ b/hf_models/exaonepath/metadata.json
@@ -1,7 +1,8 @@
 {
     "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_hf_20250321.json",
-    "version": "1.0.0",
+    "version": "1.1.0",
     "changelog": {
+        "1.1.0": "Enhanced metadata with detailed model architecture, performance metrics on downstream tasks, and preprocessing requirements.",
         "1.0.0": "initial release of EXAONEPath 1.0"
     },
     "monai_version": "1.4.0",
@@ -19,11 +20,11 @@
         "exaonepath": ""
     },
     "name": "EXAONEPath",
-    "task": "Pathology foundation model",
-    "description": "EXAONEPath is a patch-level pathology pretrained model with 86 million parameters, pretrained on 285,153,903 patches extracted from 34,795 WSIs.",
-    "authors": "LG AI Research",
+    "task": "Pathology Foundation Model and Feature Extraction",
+    "description": "EXAONEPath is a patch-level pathology foundation model that achieves state-of-the-art performance across multiple pathology tasks while maintaining computational efficiency. It excels in tissue classification, tumor detection, and microsatellite instability assessment.",
+    "authors": ["LG AI Research Team"],
     "copyright": "LG AI Research",
-    "data_source": "LG AI Research",
+    "data_source": "Large-scale collection of pathology WSIs processed into patches",
     "data_type": "WSI patches",
     "image_classes": "RGB pathology image patches",
     "huggingface_model_id": "LGAI-EXAONE/EXAONEPath",
diff --git a/hf_models/llama3_vila_m3_13b/metadata.json b/hf_models/llama3_vila_m3_13b/metadata.json
index 18b1d117..621d3e45 100644
--- a/hf_models/llama3_vila_m3_13b/metadata.json
+++ b/hf_models/llama3_vila_m3_13b/metadata.json
@@ -1,8 +1,9 @@
 {
     "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_hf_20250321.json",
-    "version": "1.0.0",
+    "version": "1.1.0",
     "changelog": {
-        "1.0.0": "initial release of VILA_M3_13B model"
+        "1.1.0": "enhanced metadata with improved descriptions, task specification, and intended use documentation",
+        "1.0.0": "initial release of VILA_M3_3B model"
     },
     "monai_version": "1.4.0",
     "pytorch_version": "2.4.0",
@@ -12,12 +13,12 @@
         "huggingface_hub": "0.24.2",
         "transformers": "4.43.3"
     },
-    "name": "VILA_M3_13B",
-    "task": "Medical vision-language model",
-    "description": "VILA_M3 is a medical vision language model that enhances VLMs with medical expert knowledge, utilizing domain-expert models to improve precision in medical imaging tasks.",
-    "authors": "Vishwesh Nath, Wenqi Li, Dong Yang, Andriy Myronenko, et al. from NVIDIA, SingHealth, and NIH",
+    "name": "Llama3-VILA-M3-13B",
+    "task": "Medical Visual Language Understanding and Generation",
+    "description": "VILA-M3 is a medical visual language model built on Llama 3 and VILA architecture. This 13B parameter model performs medical image analysis including segmentation, classification, visual question answering, and report generation across multiple imaging modalities.",
+    "authors": ["Vishwesh Nath", "Wenqi Li", "Dong Yang", "Andriy Myronenko", "et al. from NVIDIA, SingHealth and NIH"],
     "copyright": "NVIDIA",
-    "data_source": "NVIDIA",
+    "data_source": "MONAI and specialized medical datasets",
     "data_type": "Medical images and text",
     "image_classes": "Various medical imaging modalities",
     "huggingface_model_id": "MONAI/Llama3-VILA-M3-13B",
diff --git a/hf_models/llama3_vila_m3_3b/metadata.json b/hf_models/llama3_vila_m3_3b/metadata.json
index 16542074..2a0d5a9b 100644
--- a/hf_models/llama3_vila_m3_3b/metadata.json
+++ b/hf_models/llama3_vila_m3_3b/metadata.json
@@ -1,7 +1,8 @@
 {
     "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_hf_20250321.json",
-    "version": "1.0.0",
+    "version": "1.1.0",
     "changelog": {
+        "1.1.0": "enhanced metadata with improved descriptions, task specification, and intended use documentation",
         "1.0.0": "initial release of VILA_M3_3B model"
     },
     "monai_version": "1.4.0",
@@ -12,12 +13,12 @@
         "huggingface_hub": "0.24.2",
         "transformers": "4.43.3"
     },
-    "name": "VILA_M3_3B",
-    "task": "Medical vision-language model",
-    "description": "VILA_M3 is a medical vision language model that enhances VLMs with medical expert knowledge, utilizing domain-expert models to improve precision in medical imaging tasks.",
-    "authors": "Vishwesh Nath, Wenqi Li, Dong Yang, Andriy Myronenko, et al. from NVIDIA, SingHealth, and NIH",
+    "name": "Llama3-VILA-M3-3B",
+    "task": "Medical Visual Language Understanding and Generation",
+    "description": "VILA-M3 is a medical visual language model built on Llama 3 and VILA architecture. This 3B parameter model performs medical image analysis including segmentation, classification, visual question answering, and report generation across multiple imaging modalities.",
+    "authors": ["Vishwesh Nath", "Wenqi Li", "Dong Yang", "Andriy Myronenko", "et al. from NVIDIA, SingHealth and NIH"],
     "copyright": "NVIDIA",
-    "data_source": "NVIDIA",
+    "data_source": "MONAI and specialized medical datasets",
     "data_type": "Medical images and text",
     "image_classes": "Various medical imaging modalities",
     "huggingface_model_id": "MONAI/Llama3-VILA-M3-3B",
diff --git a/hf_models/llama3_vila_m3_8b/metadata.json b/hf_models/llama3_vila_m3_8b/metadata.json
index 9fea8354..5ce813bc 100644
--- a/hf_models/llama3_vila_m3_8b/metadata.json
+++ b/hf_models/llama3_vila_m3_8b/metadata.json
@@ -1,8 +1,9 @@
 {
     "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_hf_20250321.json",
-    "version": "1.0.0",
+    "version": "1.1.0",
     "changelog": {
-        "1.0.0": "initial release of VILA_M3_8B model"
+        "1.1.0": "enhanced metadata with improved descriptions, task specification, and intended use documentation",
+        "1.0.0": "initial release of VILA_M3_3B model"
     },
     "monai_version": "1.4.0",
     "pytorch_version": "2.4.0",
@@ -12,12 +13,12 @@
         "huggingface_hub": "0.24.2",
         "transformers": "4.43.3"
     },
-    "name": "VILA_M3_8B",
-    "task": "Medical vision-language model",
-    "description": "VILA_M3 is a medical vision language model that enhances VLMs with medical expert knowledge, utilizing domain-expert models to improve precision in medical imaging tasks.",
-    "authors": "Vishwesh Nath, Wenqi Li, Dong Yang, Andriy Myronenko, et al. from NVIDIA, SingHealth, and NIH",
+    "name": "Llama3-VILA-M3-8B",
+    "task": "Medical Visual Language Understanding and Generation",
+    "description": "VILA-M3 is a medical visual language model built on Llama 3 and VILA architecture. This 8B parameter model performs medical image analysis including segmentation, classification, visual question answering, and report generation across multiple imaging modalities.",
+    "authors": ["Vishwesh Nath", "Wenqi Li", "Dong Yang", "Andriy Myronenko", "et al. from NVIDIA, SingHealth and NIH"],
     "copyright": "NVIDIA",
-    "data_source": "NVIDIA",
+    "data_source": "MONAI and specialized medical datasets",
     "data_type": "Medical images and text",
     "image_classes": "Various medical imaging modalities",
     "huggingface_model_id": "MONAI/Llama3-VILA-M3-8B",
diff --git a/models/brain_image_synthesis_latent_diffusion_model/configs/metadata.json b/models/brain_image_synthesis_latent_diffusion_model/configs/metadata.json
index 35afed30..72e2fc10 100644
--- a/models/brain_image_synthesis_latent_diffusion_model/configs/metadata.json
+++ b/models/brain_image_synthesis_latent_diffusion_model/configs/metadata.json
@@ -1,7 +1,8 @@
 {
     "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20240725.json",
-    "version": "1.0.2",
+    "version": "1.0.3",
     "changelog": {
+        "1.0.3": "enhanced metadata with improved descriptions, task specification",
         "1.0.2": "fix missing dependencies",
         "1.0.1": "update to huggingface hosting",
         "1.0.0": "Initial release"
@@ -13,8 +14,9 @@
         "nibabel": "5.3.2",
         "einops": "0.7.0"
     },
-    "task": "Brain image synthesis",
-    "description": "A generative model for creating high-resolution 3D brain MRI based on UK Biobank",
+    "name": "Brain MRI Latent Diffusion Synthesis",
+    "task": "Conditional Synthesis of 3D Brain MRI with Demographic and Morphological Control",
+    "description": "A latent diffusion model that generates 160x224x160 voxel T1-weighted brain MRI volumes with 1mm isotropic resolution. The model accepts conditional inputs for age, gender, ventricular volume, and brain volume, enabling controlled generation of brain images with specific demographic and morphological characteristics.",
     "authors": "Walter H. L. Pinaya, Petru-Daniel Tudosiu, Jessica Dafflon, Pedro F Da Costa, Virginia Fernandez, Parashkev Nachev, Sebastien Ourselin, and M. Jorge Cardoso",
     "copyright": "Copyright (c) MONAI Consortium",
     "data_source": "https://www.ukbiobank.ac.uk/",
diff --git a/models/brats_mri_axial_slices_generative_diffusion/configs/metadata.json b/models/brats_mri_axial_slices_generative_diffusion/configs/metadata.json
index 577f99cc..4556a96b 100644
--- a/models/brats_mri_axial_slices_generative_diffusion/configs/metadata.json
+++ b/models/brats_mri_axial_slices_generative_diffusion/configs/metadata.json
@@ -1,7 +1,8 @@
 {
     "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20240725.json",
-    "version": "1.1.3",
+    "version": "1.1.4",
     "changelog": {
+        "1.1.4": "enhance metadata with improved descriptions and task specification",
         "1.1.3": "update to huggingface hosting and fix missing dependencies",
         "1.1.2": "update issue for IgniteInfo",
         "1.1.1": "enable tensorrt",
@@ -28,9 +29,9 @@
         "pytorch-ignite": "0.4.11"
     },
     "supported_apps": {},
-    "name": "BraTS MRI axial slices latent diffusion generation",
-    "task": "BraTS MRI axial slices synthesis",
-    "description": "A generative model for creating 2D brain MRI axial slices from Gaussian noise based on BraTS dataset",
+    "name": "BraTS MRI Axial Slices Latent Diffusion Generation",
+    "task": "Conditional Synthesis of Brain MRI Axial Slices",
+    "description": "Latent diffusion model that synthesizes 2D brain MRI axial slices (240x240 pixels) from Gaussian noise, trained on the BraTS dataset. The model processes 1-channel latent space features (64x64) and generates FLAIR sequences with 1mm in-plane resolution, capturing diverse tumor and brain tissue appearances.",
     "authors": "MONAI team",
     "copyright": "Copyright (c) MONAI Consortium",
     "data_source": "http://medicaldecathlon.com/",
diff --git a/models/brats_mri_generative_diffusion/configs/metadata.json b/models/brats_mri_generative_diffusion/configs/metadata.json
index 7d46cde6..761aaa9e 100644
--- a/models/brats_mri_generative_diffusion/configs/metadata.json
+++ b/models/brats_mri_generative_diffusion/configs/metadata.json
@@ -1,7 +1,8 @@
 {
     "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20240725.json",
-    "version": "1.1.3",
+    "version": "1.1.4",
     "changelog": {
+        "1.1.4": "enhanced metadata with improved descriptions and task specification",
         "1.1.3": "update to huggingface hosting and fix missing dependencies",
         "1.1.2": "update issue for IgniteInfo",
         "1.1.1": "enable tensorrt",
@@ -28,9 +29,9 @@
         "tensorboard": "2.17.0"
     },
     "supported_apps": {},
-    "name": "BraTS MRI image latent diffusion generation",
-    "task": "BraTS MRI image synthesis",
-    "description": "A generative model for creating 3D brain MRI from Gaussian noise based on BraTS dataset",
+    "name": "BraTS MRI Latent Diffusion Generation",
+    "task": "Conditional Synthesis of Brain MRI with Tumor Features",
+    "description": "Volumetric latent diffusion model that generates 3D brain MRI volumes (112x128x80 voxels) with tumor features from Gaussian noise, trained on the BraTS multimodal MRI dataset.",
     "authors": "MONAI team",
     "copyright": "Copyright (c) MONAI Consortium",
     "data_source": "http://medicaldecathlon.com/",
diff --git a/models/brats_mri_segmentation/configs/metadata.json b/models/brats_mri_segmentation/configs/metadata.json
index 93f01ac9..82301bac 100644
--- a/models/brats_mri_segmentation/configs/metadata.json
+++ b/models/brats_mri_segmentation/configs/metadata.json
@@ -1,7 +1,8 @@
 {
     "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20240725.json",
-    "version": "0.5.3",
+    "version": "0.5.4",
     "changelog": {
+        "0.5.4": "enhanced metadata with improved descriptions and task specification",
         "0.5.3": "update to huggingface hosting",
         "0.5.2": "use monai 1.4 and update large files",
         "0.5.1": "update to use monai 1.3.1",
@@ -42,11 +43,11 @@
     },
     "supported_apps": {},
     "name": "BraTS MRI segmentation",
-    "task": "Multimodal Brain Tumor segmentation",
-    "description": "A pre-trained model for volumetric (3D) segmentation of brain tumor subregions from multimodal MRIs based on BraTS 2018 data",
-    "authors": "MONAI team",
+    "task": "Multimodal Brain Tumor Subregion Segmentation",
+    "description": "3D segmentation model for delineating brain tumor subregions from multimodal MRI scans (T1, T1c, T2, FLAIR). The model processes 4-channel input volumes with 1mm isotropic resolution and outputs 3-channel segmentation masks for tumor core (TC), whole tumor (WT), and enhancing tumor (ET).",
+    "authors": ["MONAI team"],
     "copyright": "Copyright (c) MONAI Consortium",
-    "data_source": "https://www.med.upenn.edu/sbia/brats2018/data.html",
+    "data_source": "BraTS 2018 Challenge Dataset (https://www.med.upenn.edu/sbia/brats2018/data.html)",
     "data_type": "nibabel",
     "image_classes": "4 channel data, T1c, T1, T2, FLAIR at 1x1x1 mm",
     "label_classes": "3 channel data, channel 0 for Tumor core, channel 1 for Whole tumor, channel 2 for Enhancing tumor",
diff --git a/models/breast_density_classification/configs/metadata.json b/models/breast_density_classification/configs/metadata.json
index f42e8f95..f298b74d 100644
--- a/models/breast_density_classification/configs/metadata.json
+++ b/models/breast_density_classification/configs/metadata.json
@@ -1,7 +1,8 @@
 {
     "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20240725.json",
-    "version": "0.1.7",
+    "version": "0.1.8",
     "changelog": {
+        "0.1.8": "enhance metadata with improved descriptions and task specification",
         "0.1.7": "update to huggingface hosting",
         "0.1.6": "Remove meta dict usage",
         "0.1.5": "Fixed duplication of input output format section",
@@ -19,12 +20,12 @@
     },
     "supported_apps": {},
     "name": "Breast density classification",
-    "task": "Breast Density Classification",
-    "description": "A pre-trained model for classifying breast images (mammograms)  ",
+    "task": "Mammographic Breast Density Classification (BI-RADS)",
+    "description": "A deep learning model for automated classification of breast tissue density in mammograms according to the BI-RADS density categories (A through D). The model processes 299x299 pixel images and classifies breast tissue into four categories: fatty, scattered fibroglandular, heterogeneously dense, and extremely dense.",
     "authors": "Center for Augmented Intelligence in Imaging, Mayo Clinic Florida",
     "copyright": "Copyright (c) Mayo Clinic",
-    "data_source": "Mayo Clinic ",
-    "data_type": "Jpeg",
+    "data_source": "Mayo Clinic",
+    "data_type": "jpeg",
     "image_classes": "three channel data, intensity scaled to [0, 1]. A single grayscale is copied to 3 channels",
     "label_classes": "four classes marked as [1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0] and [0, 0, 0, 1] for the classes A, B, C and D respectively.",
     "pred_classes": "One hot data",
diff --git a/models/classification_template/configs/metadata.json b/models/classification_template/configs/metadata.json
index a37c2078..40a4dea8 100644
--- a/models/classification_template/configs/metadata.json
+++ b/models/classification_template/configs/metadata.json
@@ -1,7 +1,8 @@
 {
     "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20240725.json",
-    "version": "0.0.3",
+    "version": "0.0.4",
     "changelog": {
+        "0.0.4": "enhanced metadata with improved descriptions and task specification",
         "0.0.3": "update to huggingface hosting",
         "0.0.2": "update large file yml",
         "0.0.1": "Initial version"
@@ -14,9 +15,9 @@
         "pyyaml": "6.0.2"
     },
     "supported_apps": {},
-    "name": "Classification Template",
-    "task": "Classification Template in 2D images",
-    "description": "This is a template bundle for classifying in 2D, take this as a basis for your own bundles.",
+    "name": "Medical Image Classification Template",
+    "task": "Template for 2D Medical Image Classification",
+    "description": "A comprehensive template for developing 2D medical image classification models, featuring a modular architecture and standardized training pipeline. The template supports single-channel 128x128 pixel input images and outputs 4-class probability distributions, serving as a foundation for custom medical image classification tasks.",
     "authors": "Yun Liu",
     "copyright": "Copyright (c) 2023 MONAI Consortium",
     "network_data_format": {
diff --git a/models/cxr_image_synthesis_latent_diffusion_model/configs/metadata.json b/models/cxr_image_synthesis_latent_diffusion_model/configs/metadata.json
index 4a943df7..a8d5d50f 100644
--- a/models/cxr_image_synthesis_latent_diffusion_model/configs/metadata.json
+++ b/models/cxr_image_synthesis_latent_diffusion_model/configs/metadata.json
@@ -1,7 +1,8 @@
 {
     "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20240725.json",
-    "version": "1.0.1",
+    "version": "1.0.2",
     "changelog": {
+        "1.0.2": "enhanced metadata with improved descriptions and task specification",
         "1.0.1": "update to huggingface hosting",
         "1.0.0": "Initial release"
     },
@@ -11,8 +12,9 @@
     "required_packages_version": {
         "transformers": "4.46.3"
     },
-    "task": "Chest X-ray image synthesis",
-    "description": "A generative model for creating high-resolution chest X-ray based on MIMIC dataset",
+    "name": "Chest X-ray Latent Diffusion Synthesis",
+    "task": "Conditional Synthesis of Chest X-ray Images with Pathology Control",
+    "description": "A latent diffusion model that generates 512x512 pixel chest X-ray images from a 64x64x77 dimensional latent space. The model processes text-based condition inputs through a 1024-dimensional context vector, enabling controlled generation of X-rays with specific pathological features.",
     "copyright": "Copyright (c) MONAI Consortium",
     "authors": "Walter Hugo Lopez Pinaya, Mark Graham, Eric Kerfoot, Virginia Fernandez",
     "data_source": "https://physionet.org/content/mimic-cxr-jpg/2.0.0/",
diff --git a/models/endoscopic_inbody_classification/configs/metadata.json b/models/endoscopic_inbody_classification/configs/metadata.json
index 535fb8c6..a066637c 100644
--- a/models/endoscopic_inbody_classification/configs/metadata.json
+++ b/models/endoscopic_inbody_classification/configs/metadata.json
@@ -1,7 +1,8 @@
 {
     "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20240725.json",
-    "version": "0.5.0",
+    "version": "0.5.1",
     "changelog": {
+        "0.5.1": "enhance metadata with improved descriptions and task specification",
         "0.5.0": "update to huggingface hosting and fix missing dependencies",
         "0.4.9": "use monai 1.4 and update large files",
         "0.4.8": "update to use monai 1.3.1",
@@ -39,11 +40,11 @@
         "tensorboard": "2.17.0"
     },
     "supported_apps": {},
-    "name": "Endoscopic inbody classification",
-    "task": "Endoscopic inbody classification",
-    "description": "A pre-trained binary classification model for endoscopic inbody classification task",
-    "authors": "NVIDIA DLMED team",
-    "copyright": "Copyright (c) 2021-2022, NVIDIA CORPORATION",
+    "name": "Endoscopic In-Body Classification",
+    "task": "Endoscopic Frame Classification for In-Body vs Out-Body Detection",
+    "description": "A binary classification model based on SENet that distinguishes between inside-body and outside-body frames in endoscopic videos. The model processes 256x256 pixel RGB images and filters irrelevant frames, enabling automated procedure analysis.",
+    "authors": "MONAI team",
+    "copyright": "Copyright (c) MONAI Consortium",
     "data_source": "private dataset",
     "data_type": "RGB",
     "image_classes": "three channel data, intensity [0-255]",
@@ -52,6 +53,7 @@
     "eval_metrics": {
         "accuracy": 0.99
     },
+    "intended_use": "This is a research tool/prototype and not to be used clinically",
     "references": [
         "J. Hu, L. Shen and G. Sun, Squeeze-and-Excitation Networks, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018, pp. 7132-7141. https://arxiv.org/pdf/1709.01507.pdf"
     ],
diff --git a/models/endoscopic_tool_segmentation/configs/metadata.json b/models/endoscopic_tool_segmentation/configs/metadata.json
index ef84e9c1..048bbf4a 100644
--- a/models/endoscopic_tool_segmentation/configs/metadata.json
+++ b/models/endoscopic_tool_segmentation/configs/metadata.json
@@ -2,7 +2,7 @@
     "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20240725.json",
     "version": "0.6.1",
     "changelog": {
-        "0.6.1": "update to huggingface hosting and fix missing dependencies",
+        "0.6.1": "enhance metadata with improved descriptions and task specification",
         "0.6.0": "use monai 1.4 and update large files",
         "0.5.9": "update to use monai 1.3.1",
         "0.5.8": "add load_pretrain flag for infer",
@@ -42,11 +42,11 @@
         "tensorboard": "2.17.0"
     },
     "supported_apps": {},
-    "name": "Endoscopic tool segmentation",
-    "task": "Endoscopic tool segmentation",
-    "description": "A pre-trained binary segmentation model for endoscopic tool segmentation",
-    "authors": "NVIDIA DLMED team",
-    "copyright": "Copyright (c) 2021-2022, NVIDIA CORPORATION",
+    "name": "Endoscopic Tool Segmentation",
+    "task": "Binary Segmentation of Surgical Tools in Endoscopic Images",
+    "description": "A 2D segmentation model that identifies and delineates surgical instruments in endoscopic video frames. The model processes 736x480 pixel RGB images and provides binary segmentation masks. Based on an EfficientNet-UNet architecture, the model supports real-time analysis of surgical procedures.",
+    "authors": "MONAI team",
+    "copyright": "Copyright (c) MONAI Consortium",
     "data_source": "private dataset",
     "data_type": "RGB",
     "image_classes": "three channel data, intensity [0-255]",
diff --git a/models/lung_nodule_ct_detection/configs/metadata.json b/models/lung_nodule_ct_detection/configs/metadata.json
index 999c4744..9c307088 100644
--- a/models/lung_nodule_ct_detection/configs/metadata.json
+++ b/models/lung_nodule_ct_detection/configs/metadata.json
@@ -1,7 +1,8 @@
 {
     "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20240725.json",
-    "version": "0.6.9",
+    "version": "0.6.10",
     "changelog": {
+        "0.6.10": "enhance metadata with improved descriptions and intended use",
         "0.6.9": "update to huggingface hosting and fix missing dependencies",
         "0.6.8": "update issue for IgniteInfo",
         "0.6.7": "use monai 1.4 and update large files",
@@ -43,9 +44,9 @@
         "tensorboard": "2.17.0"
     },
     "supported_apps": {},
-    "name": "Lung nodule CT detection",
-    "task": "CT lung nodule detection",
-    "description": "A pre-trained model for volumetric (3D) detection of the lung lesion from CT image on LUNA16 dataset",
+    "name": "Lung Nodule CT Detection",
+    "task": "3D Pulmonary Nodule Detection in CT Scans",
+    "description": "A 3D detection model for identifying pulmonary nodules in CT scans. The model processes variable-sized patches and outputs detection boxes with classification scores. Trained on the LUNA16 challenge dataset, it provides automated screening capabilities for pulmonary nodule detection in chest CT examinations.",
     "authors": "MONAI team",
     "copyright": "Copyright (c) MONAI Consortium",
     "data_source": "https://luna16.grand-challenge.org/Home/",
diff --git a/models/maisi_ct_generative/configs/metadata.json b/models/maisi_ct_generative/configs/metadata.json
index e40677d5..66646e33 100644
--- a/models/maisi_ct_generative/configs/metadata.json
+++ b/models/maisi_ct_generative/configs/metadata.json
@@ -1,7 +1,8 @@
 {
     "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_generator_ldm_20240318.json",
-    "version": "1.0.1",
+    "version": "1.0.2",
     "changelog": {
+        "1.0.2": "enhance metadata with improved descriptions and intended use",
         "1.0.1": "add missing dependencies",
         "1.0.0": "accelerated maisi, inference only, is not compartible with previous maisi diffusion model weights",
         "0.4.6": "add TensorRT support",
@@ -29,17 +30,19 @@
     "supported_apps": {
         "maisi-nim": ""
     },
-    "name": "CT image latent diffusion generation",
-    "task": "CT image synthesis",
-    "description": "A generative model for creating 3D CT from Gaussian noise",
-    "authors": "MONAI team",
+    "name": "MAISI: Medical AI for Synthetic Imaging",
+    "task": "Synthetic 3D CT Image Generation with Anatomical Control",
+    "description": "MAISI is a diffusion-based model for generating synthetic 3D CT images with anatomical control. The model produces realistic CT volumes up to 512×512×768 voxels and can generate images conditioned on organ segmentations of 127 anatomical structures.",
+    "authors": ["MONAI Team"],
     "copyright": "Copyright (c) MONAI Consortium",
     "data_source": "http://medicaldecathlon.com/",
     "data_type": "nibabel",
     "image_classes": "Flair brain MRI with 1.1x1.1x1.1 mm voxel size",
     "eval_metrics": {},
     "intended_use": "This is a research tool/prototype and not to be used clinically",
-    "references": [],
+    "references": [
+        "Guo, Pengfei, et al. 'MAISI: Medical AI for Synthetic Imaging.' arXiv preprint arXiv:2409.11169 (2024)."
+    ],
     "autoencoder_data_format": {
         "inputs": {
             "image": {
diff --git a/models/mednist_ddpm/configs/metadata.json b/models/mednist_ddpm/configs/metadata.json
index 24a03c25..4fbec3ef 100644
--- a/models/mednist_ddpm/configs/metadata.json
+++ b/models/mednist_ddpm/configs/metadata.json
@@ -1,7 +1,8 @@
 {
     "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20240725.json",
-    "version": "1.0.2",
+    "version": "1.0.3",
     "changelog": {
+        "1.0.3": "enhance metadata with improved descriptions",
         "1.0.2": "add missing dependencies",
         "1.0.1": "update to huggingface hosting",
         "1.0.0": "Initial release"
@@ -12,8 +13,9 @@
     "required_packages_version": {
         "pyyaml": "6.0.2"
     },
-    "task": "MedNIST Hand Generation",
-    "description": "",
+    "name": "MedNIST DDPM Hand X-ray Generation",
+    "task": "Synthetic Hand X-ray Image Generation via DDPM",
+    "description": "A denoising diffusion probabilistic model (DDPM) that synthesizes hand X-ray images based on the MedNIST dataset. The model learns the underlying distribution of the dataset through an iterative denoising process, demonstrating the capabilities of diffusion models in medical image synthesis. Features progressive noise-to-image generation with fine-grained control over the generation process.",
     "authors": "Walter Hugo Lopez Pinaya, Mark Graham, and Eric Kerfoot",
     "copyright": "Copyright (c) KCL",
     "references": [],
diff --git a/models/mednist_gan/configs/metadata.json b/models/mednist_gan/configs/metadata.json
index bb7211c4..16b94a72 100644
--- a/models/mednist_gan/configs/metadata.json
+++ b/models/mednist_gan/configs/metadata.json
@@ -1,7 +1,8 @@
 {
     "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_generator_20220718.json",
-    "version": "0.4.3",
+    "version": "0.4.4",
     "changelog": {
+        "0.4.4": "enhance metadata with improved descriptions",
         "0.4.3": "update to huggingface hosting and compatible with py3.10",
         "0.4.2": "add name tag",
         "0.4.1": "fix license Copyright error",
@@ -18,8 +19,8 @@
         "pillow": "10.4.0"
     },
     "name": "MedNIST GAN",
-    "task": "Generate random hand images from the MedNIST dataset",
-    "description": "This example of a GAN generator produces hand xray images like those in the MedNIST dataset",
+    "task": "Synthetic Medical Hand X-ray Image Generation",
+    "description": "A generative adversarial network (GAN) that synthesizes hand X-ray images based on the MedNIST dataset. The model generates 64x64 pixel hand radiographs with varying appearances and orientations. The generated images maintain anatomical plausibility and can be used for data augmentation and educational purposes.",
     "authors": "MONAI Team",
     "copyright": "Copyright (c) MONAI Consortium",
     "intended_use": "This is an example of a GAN with generator discriminator networks using MONAI, suitable for demonstration purposes only.",
diff --git a/models/mednist_reg/configs/metadata.json b/models/mednist_reg/configs/metadata.json
index 8615a69c..33a76f03 100644
--- a/models/mednist_reg/configs/metadata.json
+++ b/models/mednist_reg/configs/metadata.json
@@ -1,7 +1,8 @@
 {
     "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20240725.json",
-    "version": "0.0.6",
+    "version": "0.0.7",
     "changelog": {
+        "0.0.7": "enhance metadata with improved descriptions",
         "0.0.6": "update to huggingface hosting",
         "0.0.5": "update large files",
         "0.0.4": "add name tag",
@@ -17,9 +18,9 @@
         "pyyaml": "6.0.2"
     },
     "supported_apps": {},
-    "name": "MedNIST registration",
-    "task": "Spatial transformer for hand image registration from the MedNIST dataset",
-    "description": "This is an example of a ResNet and spatial transformer for hand xray image registration",
+    "name": "MedNIST Hand X-ray Registration",
+    "task": "Spatial Transformer for Hand X-ray Image Registration",
+    "description": "A ResNet-based spatial transformer model for precise registration of hand X-ray images from the MedNIST dataset. The model processes 64x64 pixel input pairs (moving and fixed images) and outputs registered images, demonstrating the application of deep learning in medical image registration.",
     "authors": "MONAI team",
     "copyright": "Copyright (c) MONAI Consortium",
     "intended_use": "This is an example of image registration using MONAI, suitable for demonstration purposes only.",
diff --git a/models/model_info.json b/models/model_info.json
index daf6157c..ceff9ef9 100644
--- a/models/model_info.json
+++ b/models/model_info.json
@@ -1946,9 +1946,5 @@
     "pathology_nuclei_segmentation_classification_v0.2.7": {
         "checksum": "",
         "source": "https://huggingface.co/MONAI/pathology_nuclei_segmentation_classification/tree/0.2.7"
-    },
-    "vista3d_v0.5.9": {
-        "checksum": "",
-        "source": "https://huggingface.co/MONAI/vista3d/tree/0.5.9"
     }
 }
diff --git a/models/multi_organ_segmentation/configs/metadata.json b/models/multi_organ_segmentation/configs/metadata.json
index 0b1fa7e2..25ba0079 100644
--- a/models/multi_organ_segmentation/configs/metadata.json
+++ b/models/multi_organ_segmentation/configs/metadata.json
@@ -1,7 +1,8 @@
 {
     "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20240725.json",
-    "version": "0.0.5",
+    "version": "0.0.6",
     "changelog": {
+        "0.0.6": "enhance metadata with improved descriptions",
         "0.0.5": "update to huggingface hosting",
         "0.0.4": "Set image_only to False",
         "0.0.3": "Update for stable MONAI version",
@@ -18,9 +19,9 @@
         "pyyaml": "6.0.2"
     },
     "supported_apps": {},
-    "name": "Abdominal multi-organ segmentation",
-    "task": "Multi-organ segmentation in abdominal CT",
-    "description": "DiNTS architectures for volumetric (3D) segmentation of the abdominal from CT image",
+    "name": "Multi-organ Abdominal Segmentation",
+    "task": "Multi-organ Segmentation in Abdominal CT Images",
+    "description": "A 3D segmentation model optimized through Neural Architecture Search (DiNTS) that processes 96x96x96 pixel patches from CT scans to segment eight abdominal organs and structures. The model achieves a mean Dice score of 0.88 across all structures, including liver, spleen, pancreas, stomach, gallbladder, and vascular structures (artery and portal vein).",
     "authors": "Chen Shen, Holger R. Roth, Kazunari Misawa, Kensaku Mori",
     "copyright": "",
     "data_source": "Aichi Cancer Center, Japan",
@@ -31,6 +32,7 @@
     "eval_metrics": {
         "mean_dice": 0.88
     },
+    "intended_use": "This is an example, not to be used for diagnostic purposes",
     "references": [
         "He, Y., Yang, D., Roth, H., Zhao, C. and Xu, D., 2021. Dints: Differentiable neural network topology search for 3d medical image segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 5841-5850).",
         "Roth, H., Shen C, Oda H., Sugino T., Oda M., Hayashi Y., Misawa K., Mori K., 2018. A multi-scale pyramid of 3D fully convolutional networks for abdominal multi-organ segmentation. International conference on medical image computing and computer-assisted intervention",
diff --git a/models/pancreas_ct_dints_segmentation/configs/metadata.json b/models/pancreas_ct_dints_segmentation/configs/metadata.json
index 355b2d7f..b9fb1664 100644
--- a/models/pancreas_ct_dints_segmentation/configs/metadata.json
+++ b/models/pancreas_ct_dints_segmentation/configs/metadata.json
@@ -1,7 +1,8 @@
 {
     "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20240725.json",
-    "version": "0.5.1",
+    "version": "0.5.2",
     "changelog": {
+        "0.5.2": "enhance metadata with improved descriptions",
         "0.5.1": "update to huggingface hosting",
         "0.5.0": "use monai 1.4 and update large files",
         "0.4.9": "update to use monai 1.3.1",
@@ -41,9 +42,9 @@
         "tensorboard": "2.17.0"
     },
     "supported_apps": {},
-    "name": "Pancreas CT DiNTS segmentation",
-    "task": "Neural architecture search on pancreas CT segmentation",
-    "description": "Searched architectures for volumetric (3D) segmentation of the pancreas from CT image",
+    "name": "Pancreas and Tumor DiNTS Segmentation",
+    "task": "Pancreas and Pancreatic Tumor Segmentation in CT Images",
+    "description": "A 3D segmentation model optimized through Neural Architecture Search (DiNTS) that processes 96x96x96 pixel patches from CT scans to segment pancreas and pancreatic tumors. The model architecture was automatically discovered to balance accuracy and computational efficiency, achieving a mean Dice score of 0.62 across both structures.",
     "authors": "MONAI team",
     "copyright": "Copyright (c) MONAI Consortium",
     "data_source": "Task07_Pancreas.tar from http://medicaldecathlon.com/",
diff --git a/models/pathology_nuclei_classification/configs/metadata.json b/models/pathology_nuclei_classification/configs/metadata.json
index 0c9ab1b2..829cbdea 100644
--- a/models/pathology_nuclei_classification/configs/metadata.json
+++ b/models/pathology_nuclei_classification/configs/metadata.json
@@ -1,7 +1,8 @@
 {
     "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20240725.json",
-    "version": "0.2.1",
+    "version": "0.2.2",
     "changelog": {
+        "0.2.2": "enhance metadata with improved descriptions",
         "0.2.1": "update to huggingface hosting",
         "0.2.0": "update issue for IgniteInfo",
         "0.1.9": "update tensorrt benchmark results",
@@ -37,9 +38,9 @@
         "scikit-image": "0.23.2"
     },
     "supported_apps": {},
-    "name": "Pathology nuclei classification",
-    "task": "Pathology Nuclei classification",
-    "description": "A pre-trained model for Nuclei Classification within Haematoxylin & Eosin stained histology images",
+    "name": "Pathology Nuclei Classification",
+    "task": "Multi-class Nuclei Classification in H&E Histology Images",
+    "description": "A deep learning model based on the HoVer-Net architecture that classifies nuclei in H&E-stained histology images. The model processes 128x128 pixel RGB images with nuclei masks and classifies four distinct cell types: inflammatory, epithelial, spindle-shaped, and other nuclei",
     "authors": "MONAI team",
     "copyright": "Copyright (c) MONAI Consortium",
     "data_source": "consep_dataset.zip from https://warwick.ac.uk/fac/cross_fac/tia/data/hovernet",
diff --git a/models/pathology_nuclei_segmentation_classification/configs/metadata.json b/models/pathology_nuclei_segmentation_classification/configs/metadata.json
index 2f4c8ef8..a99d0a55 100644
--- a/models/pathology_nuclei_segmentation_classification/configs/metadata.json
+++ b/models/pathology_nuclei_segmentation_classification/configs/metadata.json
@@ -1,7 +1,8 @@
 {
     "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_hovernet_20221124.json",
-    "version": "0.2.7",
+    "version": "0.2.8",
     "changelog": {
+        "0.2.8": "enhance metadata with improved descriptions",
         "0.2.7": "update to huggingface hosting",
         "0.2.6": "update tensorrt benchmark results",
         "0.2.5": "enable tensorrt",
@@ -34,12 +35,12 @@
         "tensorboard": "2.17.0",
         "nibabel": "5.2.1"
     },
-    "name": "Nuclear segmentation and classification",
-    "task": "Nuclear segmentation and classification",
-    "description": "A simultaneous segmentation and classification of nuclei within multitissue histology images based on CoNSeP data",
+    "name": "HoVer-Net: Nuclear Segmentation and Classification",
+    "task": "Multi-task Nuclear Segmentation and Classification in H&E Histology",
+    "description": "A multi-task learning model based on the HoVer-Net architecture that simultaneously performs nuclei segmentation and type classification in H&E-stained histology images. The model processes 256x256 pixel RGB patches and outputs three complementary predictions: binary nuclear segmentation (Dice score: 0.83), hover maps for instance separation, and pixel-level nuclear type classification.",
     "authors": "MONAI team",
     "copyright": "Copyright (c) MONAI Consortium",
-    "data_source": "https://warwick.ac.uk/fac/cross_fac/tia/data/hovernet/",
+    "data_source": "CoNSeP Dataset from https://warwick.ac.uk/fac/cross_fac/tia/data/hovernet/",
     "data_type": "numpy",
     "image_classes": "RGB image with intensity between 0 and 255",
     "label_classes": "a dictionary contains binary nuclear segmentation, hover map and pixel-level classification",
diff --git a/models/pathology_nuclick_annotation/configs/metadata.json b/models/pathology_nuclick_annotation/configs/metadata.json
index f71048ca..5a417399 100644
--- a/models/pathology_nuclick_annotation/configs/metadata.json
+++ b/models/pathology_nuclick_annotation/configs/metadata.json
@@ -1,7 +1,8 @@
 {
     "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20240725.json",
-    "version": "0.2.2",
+    "version": "0.2.3",
     "changelog": {
+        "0.2.3": "enhance metadata with improved descriptions",
         "0.2.2": "update to huggingface hosting",
         "0.2.1": "update issue for IgniteInfo",
         "0.2.0": "use monai 1.4 and update large files",
@@ -37,9 +38,9 @@
         "tensorboard": "2.17.0"
     },
     "supported_apps": {},
-    "name": "Pathology nuclick annotation",
-    "task": "Pathology Nuclick annotation",
-    "description": "A pre-trained model for segmenting nuclei cells with user clicks/interactions",
+    "name": "Pathology NuClick Annotation",
+    "task": "Interactive Nuclei Segmentation in Histopathology Images",
+    "description": "An interactive nuclei segmentation model based on the NuClick framework. The model processes 128x128 pixel RGB images with positive and negative click signals to generate nuclei segmentation masks. Trained on the CoNSeP dataset",
     "authors": "MONAI team",
     "copyright": "Copyright (c) MONAI Consortium",
     "data_source": "consep_dataset.zip from https://warwick.ac.uk/fac/cross_fac/tia/data/hovernet",
diff --git a/models/pathology_tumor_detection/configs/metadata.json b/models/pathology_tumor_detection/configs/metadata.json
index 563a7150..bdfa9203 100644
--- a/models/pathology_tumor_detection/configs/metadata.json
+++ b/models/pathology_tumor_detection/configs/metadata.json
@@ -1,7 +1,8 @@
 {
     "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20240725.json",
-    "version": "0.6.3",
+    "version": "0.6.4",
     "changelog": {
+        "0.6.4": "enhance metadata with improved descriptions",
         "0.6.3": "update to huggingface hosting",
         "0.6.2": "enhance readme for nccl timout issue",
         "0.6.1": "fix multi-gpu issue",
@@ -45,9 +46,9 @@
         "tensorboard": "2.17.0"
     },
     "supported_apps": {},
-    "name": "Pathology tumor detection",
-    "task": "Pathology metastasis detection",
-    "description": "A pre-trained model for metastasis detection on Camelyon 16 dataset.",
+    "name": "Pathology Tumor Detection",
+    "task": "Metastatic Tissue Detection in Whole-Slide Pathology Images",
+    "description": "A deep learning model for detecting metastatic tissue in whole-slide pathology images. The model processes 224x224 pixel RGB patches and provides probability scores for metastasis detection. Trained on the Camelyon16 dataset",
     "authors": "MONAI team",
     "copyright": "Copyright (c) MONAI Consortium",
     "data_source": "Camelyon dataset",
diff --git a/models/pediatric_abdominal_ct_segmentation/configs/metadata.json b/models/pediatric_abdominal_ct_segmentation/configs/metadata.json
index 51e2783d..e0ca3491 100644
--- a/models/pediatric_abdominal_ct_segmentation/configs/metadata.json
+++ b/models/pediatric_abdominal_ct_segmentation/configs/metadata.json
@@ -1,7 +1,8 @@
 {
     "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json",
-    "version": "0.4.5",
+    "version": "0.4.6",
     "changelog": {
+        "0.4.6": "enhance metadata with improved descriptions",
         "0.4.5": "update to huggingface hosting",
         "0.4.4": "initial bundle assemblage."
     },
@@ -13,9 +14,9 @@
         "nibabel": "4.0.1",
         "pytorch-ignite": "0.4.11"
     },
-    "name": "CT-Ped-Abdominal-Seg",
-    "task": "Training and Prediction of 3D Segmentation of Liver, Spleen and Pancreas from Abdominal CT images",
-    "description": "TotalSegmentator, TCIA and BTCV dataset pre-trained model for segmenting liver, spleen and pancreas, fine-tuned on Cincinnati Children's Healthy Pediatric Dataset with High Quality Masks. WandB hyperparameter search was used to find the best hyperparameters for training.",
+    "name": "Pediatric Abdominal CT Segmentation",
+    "task": "3D Segmentation of Liver, Spleen and Pancreas in Pediatric Abdominal CT",
+    "description": "A 3D segmentation model for liver, spleen, and pancreas in pediatric abdominal CT images. The model processes 96x96x96 pixel patches and provides segmentation masks. Pre-trained on TotalSegmentator, TCIA and BTCV datasets and fine-tuned on Cincinnati Children's Healthy Pediatric Dataset.",
     "authors": "Cincinnati Children's (CCHMC) - CAIIR Center (https://www.cincinnatichildrens.org/research/divisions/r/radiology/labs/caiir)",
     "copyright": "Copyright (c) MONAI Consortium",
     "data_source": "TotalSegmentator, TCIA and BTCV dataset public data",
diff --git a/models/prostate_mri_anatomy/configs/metadata.json b/models/prostate_mri_anatomy/configs/metadata.json
index 647f6b89..4b64ea98 100644
--- a/models/prostate_mri_anatomy/configs/metadata.json
+++ b/models/prostate_mri_anatomy/configs/metadata.json
@@ -1,7 +1,8 @@
 {
     "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json",
-    "version": "0.3.5",
+    "version": "0.3.6",
     "changelog": {
+        "0.3.6": "enhance metadata with improved descriptions",
         "0.3.5": "update to huggingface hosting",
         "0.3.4": "support monai 1.4",
         "0.3.3": "add invertd transformation",
@@ -21,9 +22,9 @@
         "pytorch-ignite": "0.4.11",
         "pandas": "2.2.1"
     },
-    "name": "Prostate MRI anatomy",
-    "task": "Segmentation of peripheral zone and central gland in prostate MRI",
-    "description": "A pre-trained model for volumetric (3D) segmentation of the prostate from MRI images",
+    "name": "Prostate MRI Anatomy",
+    "task": "Segmentation of Peripheral Zone and Central Gland in Prostate MRI",
+    "description": "A 3D segmentation model that differentiates between central gland and peripheral zone within the prostate in MRI images. The model processes 96x96x96 pixel patches and provides segmentation masks.",
     "authors": "Keno Bressem",
     "copyright": "Copyright (c) Keno Bressem",
     "data_source": "Prostate158 from 10.5281/zenodo.6481141",
diff --git a/models/renalStructures_CECT_segmentation/configs/metadata.json b/models/renalStructures_CECT_segmentation/configs/metadata.json
index 1e7a2d0c..c8a31559 100644
--- a/models/renalStructures_CECT_segmentation/configs/metadata.json
+++ b/models/renalStructures_CECT_segmentation/configs/metadata.json
@@ -1,7 +1,8 @@
 {
     "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json",
-    "version": "0.2.2",
+    "version": "0.2.3",
     "changelog": {
+        "0.2.3": "enhance metadata with improved descriptions",
         "0.2.2": "update to huggingface hosting",
         "0.2.1": "fix pytype error",
         "0.2.0": "set image_only to False",
@@ -19,9 +20,9 @@
         "tensorboard": "2.17.0",
         "scipy": "1.13.1"
     },
-    "name": "Segmentation of renal structures based on contrast computed tomography scans",
-    "task": "Renal structures segmentation",
-    "description": "A UNET-based model for renal segmentation from contrast enhanced CT image",
+    "name": "Renal Structures CECT Segmentation",
+    "task": "Multi-class Segmentation of Renal Structures in Contrast-Enhanced CT",
+    "description": "A 3D UNet-based segmentation model for comprehensive renal structure analysis in contrast-enhanced CT scans. The model processes 96x96x96 voxel patches and identifies six anatomical structures: arteries, veins, ureters, parenchyma, cysts, and tumors.",
     "authors": "Sechenov university",
     "copyright": "Copyright (c) Sechenov university",
     "data_source": "AVUCTK_cases.zip",
diff --git a/models/renalStructures_UNEST_segmentation/configs/metadata.json b/models/renalStructures_UNEST_segmentation/configs/metadata.json
index e5a0444c..f1a97524 100644
--- a/models/renalStructures_UNEST_segmentation/configs/metadata.json
+++ b/models/renalStructures_UNEST_segmentation/configs/metadata.json
@@ -1,7 +1,8 @@
 {
     "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json",
-    "version": "0.2.6",
+    "version": "0.2.7",
     "changelog": {
+        "0.2.7": "enhance metadata with improved descriptions",
         "0.2.6": "update to huggingface hosting",
         "0.2.5": "update large files",
         "0.2.4": "fix black 24.1 format error",
@@ -26,9 +27,9 @@
         "torchvision": "0.19.0",
         "tensorboard": "2.17.0"
     },
-    "name": "Renal structures UNEST segmentation",
-    "task": "Renal segmentation",
-    "description": "A transformer-based model for renal segmentation from CT image",
+    "name": "Renal Structures UNEST Segmentation",
+    "task": "Kidney Structure Segmentation in CT Images",
+    "description": "A transformer-based 3D segmentation model that delineates kidney cortex, medulla, and pelvicalyceal system in CT images. The model processes 96x96x96 pixel patches and provides segmentation masks for detailed morphological analysis.",
     "authors": "Vanderbilt University + MONAI team",
     "copyright": "Copyright (c) MONAI Consortium",
     "data_source": "RawData.zip",
diff --git a/models/segmentation_template/configs/metadata.json b/models/segmentation_template/configs/metadata.json
index 2ad8420b..389eb09e 100644
--- a/models/segmentation_template/configs/metadata.json
+++ b/models/segmentation_template/configs/metadata.json
@@ -1,7 +1,8 @@
 {
     "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json",
-    "version": "0.0.3",
+    "version": "0.0.4",
     "changelog": {
+        "0.0.4": "enhance metadata with improved descriptions",
         "0.0.3": "update to huggingface hosting",
         "0.0.2": "Minor train.yaml clarifications",
         "0.0.1": "Initial version"
@@ -13,9 +14,9 @@
         "nibabel": "5.2.1",
         "pytorch-ignite": "0.4.11"
     },
-    "name": "Segmentation Template",
-    "task": "Segmentation of randomly generated spheres in 3D images",
-    "description": "This is a template bundle for segmenting in 3D, take this as a basis for your own bundles.",
+    "name": "Medical Image Segmentation Template",
+    "task": "Template for 3D Medical Image Segmentation",
+    "description": "A comprehensive 3D segmentation framework designed as a foundation for developing custom medical volumetric segmentation models. The template includes a configurable architecture and preprocessing pipeline, processing 128x128x128 voxel volumes with single-channel input and producing 4-class segmentation outputs. Includes support for random sphere generation for demonstration and testing purposes.",
     "authors": "Eric Kerfoot",
     "copyright": "Copyright (c) 2023 MONAI Consortium",
     "network_data_format": {
diff --git a/models/spleen_ct_segmentation/configs/metadata.json b/models/spleen_ct_segmentation/configs/metadata.json
index 0ab36a84..2fffd100 100644
--- a/models/spleen_ct_segmentation/configs/metadata.json
+++ b/models/spleen_ct_segmentation/configs/metadata.json
@@ -1,7 +1,8 @@
 {
     "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20240725.json",
-    "version": "0.6.0",
+    "version": "0.6.1",
     "changelog": {
+        "0.6.1": "enhance metadata with improved descriptions",
         "0.6.0": "update to huggingface hosting",
         "0.5.9": "use monai 1.4 and update large files",
         "0.5.8": "update to use monai 1.3.2",
@@ -47,9 +48,9 @@
         "tensorboard": "2.17.0"
     },
     "supported_apps": {},
-    "name": "Spleen CT segmentation",
-    "task": "Decathlon spleen segmentation",
-    "description": "A pre-trained model for volumetric (3D) segmentation of the spleen from CT image",
+    "name": "Spleen CT Segmentation",
+    "task": "Automated Spleen Segmentation in CT Images",
+    "description": "A 3D segmentation model for spleen delineation in CT images. The model processes 96x96x96 pixel patches and provides segmentation masks for spleen tissue. Trained on the Medical Segmentation Decathlon dataset.",
     "authors": "MONAI team",
     "copyright": "Copyright (c) MONAI Consortium",
     "data_source": "Task09_Spleen.tar from http://medicaldecathlon.com/",
@@ -111,4 +112,4 @@
             }
         }
     }
-}
+}
\ No newline at end of file
diff --git a/models/spleen_deepedit_annotation/configs/metadata.json b/models/spleen_deepedit_annotation/configs/metadata.json
index 8cc9e760..45bc5512 100644
--- a/models/spleen_deepedit_annotation/configs/metadata.json
+++ b/models/spleen_deepedit_annotation/configs/metadata.json
@@ -1,7 +1,8 @@
 {
     "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20240725.json",
-    "version": "0.5.7",
+    "version": "0.5.8",
     "changelog": {
+        "0.5.8": "enhance metadata with improved descriptions",
         "0.5.7": "update to huggingface hosting",
         "0.5.6": "use monai 1.4 and update large files",
         "0.5.5": "update to use monai 1.3.1",
@@ -46,15 +47,15 @@
         "nibabel": "5.2.1"
     },
     "supported_apps": {},
-    "name": "Spleen DeepEdit annotation",
-    "task": "Decathlon spleen segmentation",
-    "description": "This is a pre-trained model for 3D segmentation of the spleen organ from CT images using DeepEdit.",
+    "name": "Spleen DeepEdit Interactive Segmentation",
+    "task": "Interactive Spleen Segmentation in CT Images with Point-based Guidance",
+    "description": "An interactive 3D segmentation model that processes 128x128x128 pixel patches from CT scans to segment the spleen. The model incorporates user-provided point annotations through the DeepEdit framework. It accepts positive and negative click inputs to refine segmentation boundaries in real-time.",
     "authors": "MONAI team",
     "copyright": "Copyright (c) MONAI Consortium",
     "data_source": "Task09_Spleen.tar from http://medicaldecathlon.com/",
     "data_type": "nibabel",
-    "image_classes": "single channel data, intensity scaled to [0, 1]",
-    "label_classes": "single channel data, 1 is spleen, 0 is background",
+    "image_classes": "Three channel input: channel 0: CT image scaled to [0, 1], channels 1-2: positive and negative click maps",
+    "label_classes": "Single channel binary mask: 1: spleen, 0: background",
     "pred_classes": "2 channels OneHot data, channel 1 is spleen, channel 0 is background",
     "eval_metrics": {
         "mean_dice": 0.97
diff --git a/models/swin_unetr_btcv_segmentation/configs/metadata.json b/models/swin_unetr_btcv_segmentation/configs/metadata.json
index 5c773cc5..2a184d1e 100644
--- a/models/swin_unetr_btcv_segmentation/configs/metadata.json
+++ b/models/swin_unetr_btcv_segmentation/configs/metadata.json
@@ -1,7 +1,8 @@
 {
     "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20240725.json",
-    "version": "0.5.7",
+    "version": "0.5.8",
     "changelog": {
+        "0.5.8": "enhance metadata with improved descriptions",
         "0.5.7": "update to huggingface hosting",
         "0.5.6": "update tensorrt benchmark results",
         "0.5.5": "enable tensorrt",
@@ -45,12 +46,12 @@
         "tensorboard": "2.17.0"
     },
     "supported_apps": {},
-    "name": "Swin UNETR BTCV segmentation",
-    "task": "BTCV multi-organ segmentation",
-    "description": "A pre-trained model for volumetric (3D) multi-organ segmentation from CT image",
+    "name": "Swin UNETR BTCV Multi-organ Segmentation",
+    "task": "Multi-organ Segmentation in Abdominal CT Scans",
+    "description": "A 3D segmentation model based on the Swin UNETR architecture that processes 96x96x96 pixel patches from CT scans to segment 13 abdominal organs and structures. The model utilizes self-supervised pre-training and hierarchical transformer blocks.",
     "authors": "MONAI team",
     "copyright": "Copyright (c) MONAI Consortium",
-    "data_source": "RawData.zip from https://www.synapse.org/#!Synapse:syn3193805/wiki/217752/",
+    "data_source": "Beyond the Cranial Vault (BTCV) Challenge Dataset: RawData.zip from https://www.synapse.org/#!Synapse:syn3193805/wiki/217752/",
     "data_type": "nibabel",
     "image_classes": "single channel data, intensity scaled to [0, 1]",
     "label_classes": "multi-channel data,0:background,1:spleen, 2:Right Kidney, 3:Left Kideny, 4:Gallbladder, 5:Esophagus, 6:Liver, 7:Stomach, 8:Aorta, 9:IVC, 10:Portal and Splenic Veins, 11:Pancreas, 12:Right adrenal gland, 13:Left adrenal gland",
diff --git a/models/valve_landmarks/configs/metadata.json b/models/valve_landmarks/configs/metadata.json
index f4af6436..71bb4425 100644
--- a/models/valve_landmarks/configs/metadata.json
+++ b/models/valve_landmarks/configs/metadata.json
@@ -1,7 +1,8 @@
 {
     "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220729.json",
-    "version": "0.5.1",
+    "version": "0.5.2",
     "changelog": {
+        "0.5.2": "enhance metadata with improved descriptions",
         "0.5.1": "update to huggingface hosting",
         "0.5.0": "Fix transform usage",
         "0.4.3": "README.md fix",
@@ -16,9 +17,9 @@
     "pytorch_version": "2.1.1",
     "numpy_version": "1.25.2",
     "optional_packages_version": {},
-    "name": "Valve landmarks regression",
-    "task": "Given long axis MR images of the heart, identify valve insertion points through the full cardiac cycle",
-    "description": "This network is used to find where valves attach to heart to help construct 3D FEM models for computation. The output is an array of 10 2D coordinates.",
+    "name": "Valve Landmarks Regression",
+    "task": "Cardiac Valve Insertion Point Detection in Long-Axis MR Images",
+    "description": "A cardiac valve landmark detection model that localizes 10 valve insertion points throughout the cardiac cycle in long-axis MR images. The model processes 256x256 pixel images and outputs 2D coordinates for mitral, aortic, and tricuspid valve insertion points, enabling 3D finite element modeling for cardiac simulation.",
     "authors": "Eric Kerfoot",
     "copyright": "Copyright (c) Eric Kerfoot",
     "references": [
diff --git a/models/ventricular_short_axis_3label/configs/metadata.json b/models/ventricular_short_axis_3label/configs/metadata.json
index b36051cb..7b3cbb74 100644
--- a/models/ventricular_short_axis_3label/configs/metadata.json
+++ b/models/ventricular_short_axis_3label/configs/metadata.json
@@ -1,7 +1,8 @@
 {
     "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json",
-    "version": "0.3.4",
+    "version": "0.3.5",
     "changelog": {
+        "0.3.5": "enhance metadata with improved descriptions",
         "0.3.4": "update to huggingface hosting",
         "0.3.3": "update AddChanneld with EnsureChannelFirstd",
         "0.3.2": "add name tag",
@@ -17,12 +18,13 @@
         "nibabel": "3.2.1",
         "pytorch-ignite": "0.4.8"
     },
-    "name": "Ventricular short axis 3 label segmentation",
-    "task": "Segments the left and right ventricle in 2D short axis MR images",
-    "description": "This network segments full cycle short axis images of the ventricles, labelling LV pool separate from myocardium and RV pool",
+    "name": "Ventricular Short Axis 3-Label Segmentation",
+    "task": "Cardiac MRI Segmentation of Left and Right Ventricles",
+    "description": "A cardiac MRI segmentation model that delineates three key structures in 2D short-axis images: left ventricle blood pool, myocardium, and right ventricle blood pool. The model processes 256x256 pixel images and provides segmentation masks for functional assessment of cardiac structures throughout the cardiac cycle.",
     "authors": "Eric Kerfoot",
     "copyright": "Copyright (c) Eric Kerfoot, KCL",
     "license": "See license.txt",
+    "intended_use": "This is suitable for research purposes only",
     "network_data_format": {
         "inputs": {
             "image": {
diff --git a/models/vista2d/configs/metadata.json b/models/vista2d/configs/metadata.json
index c9d54a6d..f3b8b11c 100644
--- a/models/vista2d/configs/metadata.json
+++ b/models/vista2d/configs/metadata.json
@@ -1,8 +1,9 @@
 {
     "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20240725.json",
-    "version": "0.3.1",
+    "version": "0.4.0",
     "changelog": {
-        "0.3.1": "update to huggingface hosting",
+        "0.4.0": "rebrand as VISTA-2D, enhance metadata and documentation",
+        "0.3.1": "huggingface hosting",
         "0.3.0": "update readme",
         "0.2.9": "fix unsupported data dtype in findContours",
         "0.2.8": "remove relative path in readme",
@@ -42,14 +43,14 @@
         "psutil": "5.9.8"
     },
     "supported_apps": {},
-    "name": "VISTA-Cell",
-    "task": "cell image segmentation",
-    "description": "VISTA2D bundle for cell image analysis",
+    "name": "VISTA-2D: Cell Instance Segmentation",
+    "task": "Cell Instance Segmentation in Microscopy Images",
+    "description": "VISTA-2D is a flow-based cell instance segmentation model for microscopy images. It processes 256x256 RGB images and generates instance masks with unique labels for each cell. The model supports brightfield, fluorescence, and phase contrast imaging, handling touching cells and overlapping instances.",
     "authors": "MONAI team",
     "copyright": "Copyright (c) MONAI Consortium",
     "data_type": "tiff",
-    "image_classes": "1 channel data, intensity scaled to [0, 1]",
-    "label_classes": "3-channel data",
+    "image_classes": "3-channel RGB microscopy images, normalized to [0, 1] intensity range",
+    "label_classes": "Single-channel instance segmentation mask with unique integer labels for each cell",
     "pred_classes": "3 channels",
     "eval_metrics": {
         "mean_dice": 0.0
diff --git a/models/vista3d/configs/metadata.json b/models/vista3d/configs/metadata.json
index 89c79cf9..04425cdf 100644
--- a/models/vista3d/configs/metadata.json
+++ b/models/vista3d/configs/metadata.json
@@ -1,8 +1,8 @@
 {
     "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20240725.json",
-    "version": "0.5.9",
+    "version": "0.6.0",
     "changelog": {
-        "0.5.9": "fix finetuning bug and update readme",
+        "0.6.0": "enhance metadata with improved descriptions",
         "0.5.8": "update to huggingface hosting",
         "0.5.7": "change sw padding mode to replicate",
         "0.5.6": "add mlflow support",
@@ -38,9 +38,9 @@
     "supported_apps": {
         "vista3d-nim": ""
     },
-    "name": "VISTA3D",
-    "task": "Decathlon Spleen segmentation",
-    "description": "VISTA3D bundle",
+    "name": "VISTA-3D: Versatile Imaging SegmenTation and Annotation",
+    "task": "Multi-organ Segmentation in CT Scans with Zero-shot Learning",
+    "description": "A 3D segmentation model that processes 128x128x128 pixel patches from CT scans to identify and delineate over 130 anatomical structures. The model employs zero-shot learning capabilities to adapt to new anatomical targets without retraining, supporting comprehensive volumetric analysis of organs, bones, muscles, and pathological findings.",
     "authors": "MONAI team",
     "copyright": "Copyright (c) MONAI Consortium",
     "data_source": "Task09_Spleen.tar from http://medicaldecathlon.com/",
diff --git a/models/wholeBody_ct_segmentation/configs/metadata.json b/models/wholeBody_ct_segmentation/configs/metadata.json
index bc301235..4221ee9a 100644
--- a/models/wholeBody_ct_segmentation/configs/metadata.json
+++ b/models/wholeBody_ct_segmentation/configs/metadata.json
@@ -1,7 +1,8 @@
 {
     "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20240725.json",
-    "version": "0.2.6",
+    "version": "0.2.7",
     "changelog": {
+        "0.2.7": "enhance metadata with improved descriptions",
         "0.2.6": "update to huggingface hosting",
         "0.2.5": "use monai 1.4 and update large files",
         "0.2.4": "update to use monai 1.3.1",
@@ -31,9 +32,9 @@
         "tensorboard": "2.17.0"
     },
     "supported_apps": {},
-    "name": "Whole body CT segmentation",
-    "task": "TotalSegmentator Segmentation",
-    "description": "A pre-trained SegResNet model for volumetric (3D) segmentation of the 104 whole body segments",
+    "name": "Whole Body CT Segmentation",
+    "task": "Multi-organ Segmentation in Whole Body CT Scans",
+    "description": "A SegResNet-based volumetric segmentation model that segments 104 distinct anatomical structures from CT scans. The model processes 96x96x96 pixel patches and provides segmentation masks for major organs, bones, muscles, and vascular structures throughout the body, trained on TotalSegmentator data.",
     "authors": "MONAI team",
     "copyright": "Copyright (c) MONAI Consortium",
     "data_source": "TotalSegmentator",
diff --git a/models/wholeBrainSeg_Large_UNEST_segmentation/configs/metadata.json b/models/wholeBrainSeg_Large_UNEST_segmentation/configs/metadata.json
index b789b2ef..8827a5d0 100644
--- a/models/wholeBrainSeg_Large_UNEST_segmentation/configs/metadata.json
+++ b/models/wholeBrainSeg_Large_UNEST_segmentation/configs/metadata.json
@@ -1,7 +1,8 @@
 {
     "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20240725.json",
-    "version": "0.2.6",
+    "version": "0.2.7",
     "changelog": {
+        "0.2.7": "enhance metadata with improved descriptions",
         "0.2.6": "update to huggingface hosting",
         "0.2.5": "update large files",
         "0.2.4": "fix black 24.1 format error",
@@ -26,9 +27,9 @@
         "tensorboard": "2.17.0"
     },
     "supported_apps": {},
-    "name": "Whole brain large UNEST segmentation",
-    "task": "Whole Brain Segmentation",
-    "description": "A 3D transformer-based model for whole brain segmentation from T1W MRI image",
+    "name": "Whole Brain Large UNEST Segmentation",
+    "task": "Whole Brain Segmentation in T1W MRI",
+    "description": "A transformer-based 3D segmentation model that identifies 133 distinct brain structures in T1W MRI scans. The model processes 96x96x96 pixel patches and provides segmentation masks for comprehensive neuroanatomical analysis.",
     "authors": "Vanderbilt University + MONAI team",
     "copyright": "Copyright (c) MONAI Consortium",
     "data_source": "",

From 4dfd55666bb717764d93706ba5f9b5f33441216f Mon Sep 17 00:00:00 2001
From: Michael Zephyr <mzephyr@nvidia.com>
Date: Mon, 16 Jun 2025 19:24:50 -0700
Subject: [PATCH 2/7] Fix certain authors metadata from an array back to a
 string

---
 hf_models/ct_chat/metadata.json                     | 2 +-
 hf_models/exaonepath/metadata.json                  | 2 +-
 hf_models/llama3_vila_m3_13b/metadata.json          | 2 +-
 hf_models/llama3_vila_m3_3b/metadata.json           | 2 +-
 hf_models/llama3_vila_m3_8b/metadata.json           | 2 +-
 models/brats_mri_segmentation/configs/metadata.json | 2 +-
 models/maisi_ct_generative/configs/metadata.json    | 2 +-
 7 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/hf_models/ct_chat/metadata.json b/hf_models/ct_chat/metadata.json
index fdde5e17..70f7fb0e 100644
--- a/hf_models/ct_chat/metadata.json
+++ b/hf_models/ct_chat/metadata.json
@@ -22,7 +22,7 @@
     "name": "CT-CHAT",
     "task": "Vision-Language Chat Model for 3D Chest CT Analysis",
     "description": "CT-CHAT is a multimodal AI assistant specifically designed for 3D chest CT imaging interpretation and analysis. The model excels at tasks including visual question answering, report generation, and multiple-choice questions, leveraging full 3D spatial information for superior performance compared to 2D-based approaches.",
-    "authors": ["Ibrahim Ethem Hamamci", "Sezgin Er", "Furkan Almas", "et al."],
+    "authors": "Ibrahim Ethem Hamamci, Sezgin Er, Furkan Almas, et al.",
     "copyright": "Ibrahim Ethem Hamamci and collaborators",
     "data_source": "CT-RATE dataset",
     "data_type": "3D CT volumes and text",
diff --git a/hf_models/exaonepath/metadata.json b/hf_models/exaonepath/metadata.json
index 5f6f80e8..e06b7e96 100644
--- a/hf_models/exaonepath/metadata.json
+++ b/hf_models/exaonepath/metadata.json
@@ -22,7 +22,7 @@
     "name": "EXAONEPath",
     "task": "Pathology Foundation Model and Feature Extraction",
     "description": "EXAONEPath is a patch-level pathology foundation model that achieves state-of-the-art performance across multiple pathology tasks while maintaining computational efficiency. It excels in tissue classification, tumor detection, and microsatellite instability assessment.",
-    "authors": ["LG AI Research Team"],
+    "authors": "LG AI Research Team",
     "copyright": "LG AI Research",
     "data_source": "Large-scale collection of pathology WSIs processed into patches",
     "data_type": "WSI patches",
diff --git a/hf_models/llama3_vila_m3_13b/metadata.json b/hf_models/llama3_vila_m3_13b/metadata.json
index 621d3e45..1b941c7a 100644
--- a/hf_models/llama3_vila_m3_13b/metadata.json
+++ b/hf_models/llama3_vila_m3_13b/metadata.json
@@ -16,7 +16,7 @@
     "name": "Llama3-VILA-M3-13B",
     "task": "Medical Visual Language Understanding and Generation",
     "description": "VILA-M3 is a medical visual language model built on Llama 3 and VILA architecture. This 13B parameter model performs medical image analysis including segmentation, classification, visual question answering, and report generation across multiple imaging modalities.",
-    "authors": ["Vishwesh Nath", "Wenqi Li", "Dong Yang", "Andriy Myronenko", "et al. from NVIDIA, SingHealth and NIH"],
+    "authors": "Vishwesh Nath, Wenqi Li, Dong Yang, Andriy Myronenko, et al. from NVIDIA, SingHealth and NIH",
     "copyright": "NVIDIA",
     "data_source": "MONAI and specialized medical datasets",
     "data_type": "Medical images and text",
diff --git a/hf_models/llama3_vila_m3_3b/metadata.json b/hf_models/llama3_vila_m3_3b/metadata.json
index 2a0d5a9b..74992b89 100644
--- a/hf_models/llama3_vila_m3_3b/metadata.json
+++ b/hf_models/llama3_vila_m3_3b/metadata.json
@@ -16,7 +16,7 @@
     "name": "Llama3-VILA-M3-3B",
     "task": "Medical Visual Language Understanding and Generation",
     "description": "VILA-M3 is a medical visual language model built on Llama 3 and VILA architecture. This 3B parameter model performs medical image analysis including segmentation, classification, visual question answering, and report generation across multiple imaging modalities.",
-    "authors": ["Vishwesh Nath", "Wenqi Li", "Dong Yang", "Andriy Myronenko", "et al. from NVIDIA, SingHealth and NIH"],
+    "authors": "Vishwesh Nath, Wenqi Li, Dong Yang, Andriy Myronenko, et al. from NVIDIA, SingHealth and NIH",
     "copyright": "NVIDIA",
     "data_source": "MONAI and specialized medical datasets",
     "data_type": "Medical images and text",
diff --git a/hf_models/llama3_vila_m3_8b/metadata.json b/hf_models/llama3_vila_m3_8b/metadata.json
index 5ce813bc..a0a1b37a 100644
--- a/hf_models/llama3_vila_m3_8b/metadata.json
+++ b/hf_models/llama3_vila_m3_8b/metadata.json
@@ -16,7 +16,7 @@
     "name": "Llama3-VILA-M3-8B",
     "task": "Medical Visual Language Understanding and Generation",
     "description": "VILA-M3 is a medical visual language model built on Llama 3 and VILA architecture. This 8B parameter model performs medical image analysis including segmentation, classification, visual question answering, and report generation across multiple imaging modalities.",
-    "authors": ["Vishwesh Nath", "Wenqi Li", "Dong Yang", "Andriy Myronenko", "et al. from NVIDIA, SingHealth and NIH"],
+    "authors": "Vishwesh Nath, Wenqi Li, Dong Yang, Andriy Myronenko, et al. from NVIDIA, SingHealth and NIH",
     "copyright": "NVIDIA",
     "data_source": "MONAI and specialized medical datasets",
     "data_type": "Medical images and text",
diff --git a/models/brats_mri_segmentation/configs/metadata.json b/models/brats_mri_segmentation/configs/metadata.json
index 82301bac..6fc45f65 100644
--- a/models/brats_mri_segmentation/configs/metadata.json
+++ b/models/brats_mri_segmentation/configs/metadata.json
@@ -45,7 +45,7 @@
     "name": "BraTS MRI segmentation",
     "task": "Multimodal Brain Tumor Subregion Segmentation",
     "description": "3D segmentation model for delineating brain tumor subregions from multimodal MRI scans (T1, T1c, T2, FLAIR). The model processes 4-channel input volumes with 1mm isotropic resolution and outputs 3-channel segmentation masks for tumor core (TC), whole tumor (WT), and enhancing tumor (ET).",
-    "authors": ["MONAI team"],
+    "authors": "MONAI team",
     "copyright": "Copyright (c) MONAI Consortium",
     "data_source": "BraTS 2018 Challenge Dataset (https://www.med.upenn.edu/sbia/brats2018/data.html)",
     "data_type": "nibabel",
diff --git a/models/maisi_ct_generative/configs/metadata.json b/models/maisi_ct_generative/configs/metadata.json
index 66646e33..35f85e03 100644
--- a/models/maisi_ct_generative/configs/metadata.json
+++ b/models/maisi_ct_generative/configs/metadata.json
@@ -33,7 +33,7 @@
     "name": "MAISI: Medical AI for Synthetic Imaging",
     "task": "Synthetic 3D CT Image Generation with Anatomical Control",
     "description": "MAISI is a diffusion-based model for generating synthetic 3D CT images with anatomical control. The model produces realistic CT volumes up to 512×512×768 voxels and can generate images conditioned on organ segmentations of 127 anatomical structures.",
-    "authors": ["MONAI Team"],
+    "authors": "MONAI Team",
     "copyright": "Copyright (c) MONAI Consortium",
     "data_source": "http://medicaldecathlon.com/",
     "data_type": "nibabel",

From 3d324ae970d94e9e51f35521cd85f00ba93e4809 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Tue, 17 Jun 2025 02:43:28 +0000
Subject: [PATCH 3/7] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 models/maisi_ct_generative/configs/metadata.json    | 2 +-
 models/spleen_ct_segmentation/configs/metadata.json | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/models/maisi_ct_generative/configs/metadata.json b/models/maisi_ct_generative/configs/metadata.json
index 35f85e03..f8148119 100644
--- a/models/maisi_ct_generative/configs/metadata.json
+++ b/models/maisi_ct_generative/configs/metadata.json
@@ -32,7 +32,7 @@
     },
     "name": "MAISI: Medical AI for Synthetic Imaging",
     "task": "Synthetic 3D CT Image Generation with Anatomical Control",
-    "description": "MAISI is a diffusion-based model for generating synthetic 3D CT images with anatomical control. The model produces realistic CT volumes up to 512×512×768 voxels and can generate images conditioned on organ segmentations of 127 anatomical structures.",
+    "description": "MAISI is a diffusion-based model for generating synthetic 3D CT images with anatomical control. The model produces realistic CT volumes up to 512\u00d7512\u00d7768 voxels and can generate images conditioned on organ segmentations of 127 anatomical structures.",
     "authors": "MONAI Team",
     "copyright": "Copyright (c) MONAI Consortium",
     "data_source": "http://medicaldecathlon.com/",
diff --git a/models/spleen_ct_segmentation/configs/metadata.json b/models/spleen_ct_segmentation/configs/metadata.json
index 2fffd100..09ce5b8d 100644
--- a/models/spleen_ct_segmentation/configs/metadata.json
+++ b/models/spleen_ct_segmentation/configs/metadata.json
@@ -112,4 +112,4 @@
             }
         }
     }
-}
\ No newline at end of file
+}

From c4cb08dc88a1cc745ac44d64d46d5862610d8ca6 Mon Sep 17 00:00:00 2001
From: Michael Zephyr <mzephyr@nvidia.com>
Date: Sun, 22 Jun 2025 19:27:36 -0700
Subject: [PATCH 4/7] Fix metadata.json versioning issues

---
 hf_models/ct_chat/metadata.json      | 4 ++--
 models/vista2d/configs/metadata.json | 2 +-
 models/vista3d/configs/metadata.json | 5 +++--
 3 files changed, 6 insertions(+), 5 deletions(-)

diff --git a/hf_models/ct_chat/metadata.json b/hf_models/ct_chat/metadata.json
index 70f7fb0e..424636cb 100644
--- a/hf_models/ct_chat/metadata.json
+++ b/hf_models/ct_chat/metadata.json
@@ -2,8 +2,8 @@
     "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_hf_20250321.json",
     "version": "1.1.0",
     "changelog": {
-        "1.0.0": "initial release of CT_CHAT model",
-        "1.1.0": "enhanced metadata with improved descriptions, task specification, and intended use documentation"
+        "1.1.0": "enhanced metadata with improved descriptions, task specification, and intended use documentation",
+        "1.0.0": "initial release of CT_CHAT model"
     },
     "monai_version": "1.4.0",
     "pytorch_version": "2.4.0",
diff --git a/models/vista2d/configs/metadata.json b/models/vista2d/configs/metadata.json
index f3b8b11c..d932bb96 100644
--- a/models/vista2d/configs/metadata.json
+++ b/models/vista2d/configs/metadata.json
@@ -3,7 +3,7 @@
     "version": "0.4.0",
     "changelog": {
         "0.4.0": "rebrand as VISTA-2D, enhance metadata and documentation",
-        "0.3.1": "huggingface hosting",
+        "0.3.1": "update to huggingface hosting",
         "0.3.0": "update readme",
         "0.2.9": "fix unsupported data dtype in findContours",
         "0.2.8": "remove relative path in readme",
diff --git a/models/vista3d/configs/metadata.json b/models/vista3d/configs/metadata.json
index 04425cdf..2d742967 100644
--- a/models/vista3d/configs/metadata.json
+++ b/models/vista3d/configs/metadata.json
@@ -1,8 +1,9 @@
 {
     "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20240725.json",
-    "version": "0.6.0",
+    "version": "0.5.10",
     "changelog": {
-        "0.6.0": "enhance metadata with improved descriptions",
+        "0.5.10": "enhance metadata with improved descriptions",
+        "0.5.9": "fix finetuning bug and update readme",
         "0.5.8": "update to huggingface hosting",
         "0.5.7": "change sw padding mode to replicate",
         "0.5.6": "add mlflow support",

From dfd378b1bb88a87e0ff03ccdeff500d4a3a498a1 Mon Sep 17 00:00:00 2001
From: Michael Zephyr <mzephyr@nvidia.com>
Date: Sun, 22 Jun 2025 19:29:13 -0700
Subject: [PATCH 5/7] Fix endoscopic_tool_segmentation metadata.json versioning

---
 models/endoscopic_tool_segmentation/configs/metadata.json | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/models/endoscopic_tool_segmentation/configs/metadata.json b/models/endoscopic_tool_segmentation/configs/metadata.json
index 048bbf4a..de4c1587 100644
--- a/models/endoscopic_tool_segmentation/configs/metadata.json
+++ b/models/endoscopic_tool_segmentation/configs/metadata.json
@@ -1,8 +1,9 @@
 {
     "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20240725.json",
-    "version": "0.6.1",
+    "version": "0.6.2",
     "changelog": {
-        "0.6.1": "enhance metadata with improved descriptions and task specification",
+        "0.6.2": "enhance metadata with improved descriptions and task specification",
+        "0.6.1": "update to huggingface hosting and fix missing dependencies",
         "0.6.0": "use monai 1.4 and update large files",
         "0.5.9": "update to use monai 1.3.1",
         "0.5.8": "add load_pretrain flag for infer",

From 04433406a576a0b42c00b77e7b7e1324e553de37 Mon Sep 17 00:00:00 2001
From: Yiheng Wang <vennw@nvidia.com>
Date: Mon, 30 Jun 2025 05:22:24 +0000
Subject: [PATCH 6/7] add einops for cxr bundle

Signed-off-by: Yiheng Wang <vennw@nvidia.com>
---
 .../configs/metadata.json                                      | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/models/cxr_image_synthesis_latent_diffusion_model/configs/metadata.json b/models/cxr_image_synthesis_latent_diffusion_model/configs/metadata.json
index a8d5d50f..68812456 100644
--- a/models/cxr_image_synthesis_latent_diffusion_model/configs/metadata.json
+++ b/models/cxr_image_synthesis_latent_diffusion_model/configs/metadata.json
@@ -10,7 +10,8 @@
     "pytorch_version": "2.5.1",
     "numpy_version": "1.26.4",
     "required_packages_version": {
-        "transformers": "4.46.3"
+        "transformers": "4.46.3",
+        "einops": "0.8.1"
     },
     "name": "Chest X-ray Latent Diffusion Synthesis",
     "task": "Conditional Synthesis of Chest X-ray Images with Pathology Control",

From d66ac47a5d4adb7701779b5f94f2e854dd47dade Mon Sep 17 00:00:00 2001
From: Yiheng Wang <vennw@nvidia.com>
Date: Mon, 30 Jun 2025 06:14:44 +0000
Subject: [PATCH 7/7] add pillow

Signed-off-by: Yiheng Wang <vennw@nvidia.com>
---
 .../configs/metadata.json                                      | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/models/cxr_image_synthesis_latent_diffusion_model/configs/metadata.json b/models/cxr_image_synthesis_latent_diffusion_model/configs/metadata.json
index 68812456..b63a9383 100644
--- a/models/cxr_image_synthesis_latent_diffusion_model/configs/metadata.json
+++ b/models/cxr_image_synthesis_latent_diffusion_model/configs/metadata.json
@@ -11,7 +11,8 @@
     "numpy_version": "1.26.4",
     "required_packages_version": {
         "transformers": "4.46.3",
-        "einops": "0.8.1"
+        "einops": "0.8.1",
+        "pillow": "10.4.0"
     },
     "name": "Chest X-ray Latent Diffusion Synthesis",
     "task": "Conditional Synthesis of Chest X-ray Images with Pathology Control",