Fix certain authors metadata from an array back to a string

Author: zephyrie

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",

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",

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",

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",

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",

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",

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",