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Author: pre-commit-ci[bot]

hf_models/ct_chat/metadata.json

@@ -22,7 +22,12 @@
     "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",

hf_models/exaonepath/metadata.json

@@ -22,7 +22,9 @@
     "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",

hf_models/llama3_vila_m3_13b/metadata.json

@@ -16,7 +16,13 @@
     "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",

hf_models/llama3_vila_m3_3b/metadata.json

@@ -16,7 +16,13 @@
     "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",

hf_models/llama3_vila_m3_8b/metadata.json

@@ -16,7 +16,13 @@
     "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",

models/brats_mri_segmentation/configs/metadata.json

@@ -45,7 +45,9 @@
     "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",

models/maisi_ct_generative/configs/metadata.json

@@ -32,8 +32,10 @@
     },
     "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"],
+    "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/",
     "data_type": "nibabel",

models/spleen_ct_segmentation/configs/metadata.json

@@ -112,4 +112,4 @@
             }
         }
     }
-}
+}