| { |
| "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" |
| } |
| } |
| } |
| } |
| } |
|
|
