| { | |
| "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20240725.json", | |
| "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", | |
| "0.1.4": "Changed Readme", | |
| "0.1.3": "Change input_dim from 229 to 299", | |
| "0.1.2": "black autofix format and add name tag", | |
| "0.1.1": "update license files", | |
| "0.1.0": "complete the model package" | |
| }, | |
| "monai_version": "1.3.0", | |
| "pytorch_version": "1.13.1", | |
| "numpy_version": "1.22.2", | |
| "required_packages_version": { | |
| "torchvision": "0.14.1" | |
| }, | |
| "supported_apps": {}, | |
| "name": "Breast density classification", | |
| "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", | |
| "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", | |
| "eval_metrics": { | |
| "accuracy": 0.96 | |
| }, | |
| "intended_use": "This is an example, not to be used for diagnostic purposes", | |
| "references": [ | |
| "Gupta, Vikash, et al. A multi-reconstruction study of breast density estimation using Deep Learning. arXiv preprint arXiv:2202.08238 (2022)." | |
| ], | |
| "network_data_format": { | |
| "inputs": { | |
| "image": { | |
| "type": "image", | |
| "format": "magnitude", | |
| "modality": "Mammogram", | |
| "num_channels": 3, | |
| "spatial_shape": [ | |
| 299, | |
| 299 | |
| ], | |
| "dtype": "float32", | |
| "value_range": [ | |
| 0, | |
| 1 | |
| ], | |
| "is_patch_data": false, | |
| "channel_def": { | |
| "0": "image" | |
| } | |
| } | |
| }, | |
| "outputs": { | |
| "pred": { | |
| "type": "image", | |
| "format": "labels", | |
| "dtype": "float32", | |
| "value_range": [ | |
| 0, | |
| 1 | |
| ], | |
| "num_channels": 4, | |
| "spatial_shape": [ | |
| 1, | |
| 4 | |
| ], | |
| "is_patch_data": false, | |
| "channel_def": { | |
| "0": "A", | |
| "1": "B", | |
| "2": "C", | |
| "3": "D" | |
| } | |
| } | |
| } | |
| } | |
| } | |