Upload segmentation_template version 0.0.4
Browse files- configs/metadata.json +5 -4
configs/metadata.json
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@@ -1,7 +1,8 @@
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{
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"schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json",
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"version": "0.0.
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"changelog": {
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"0.0.3": "update to huggingface hosting",
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"0.0.2": "Minor train.yaml clarifications",
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"0.0.1": "Initial version"
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@@ -13,9 +14,9 @@
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"nibabel": "5.2.1",
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"pytorch-ignite": "0.4.11"
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},
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"name": "Segmentation Template",
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"task": "
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"description": "
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"authors": "Eric Kerfoot",
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"copyright": "Copyright (c) 2023 MONAI Consortium",
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"network_data_format": {
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{
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"schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json",
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"version": "0.0.4",
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"changelog": {
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"0.0.4": "enhance metadata with improved descriptions",
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"0.0.3": "update to huggingface hosting",
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"0.0.2": "Minor train.yaml clarifications",
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"0.0.1": "Initial version"
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"nibabel": "5.2.1",
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"pytorch-ignite": "0.4.11"
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},
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"name": "Medical Image Segmentation Template",
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"task": "Template for 3D Medical Image Segmentation",
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"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.",
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"authors": "Eric Kerfoot",
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"copyright": "Copyright (c) 2023 MONAI Consortium",
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"network_data_format": {
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