{
    "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json",
    "version": "0.1.5",
    "changelog": {
        "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": "1.0.0",
    "pytorch_version": "1.12.1",
    "numpy_version": "1.21.2",
    "optional_packages_version": {
        "torchvision": "0.13.1"
    },
    "name": "Breast density classification",
    "task": "Breast Density Classification",
    "description": "A pre-trained model for classifying breast images (mammograms)  ",
    "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"
                }
            }
        }
    }
}