{
    "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json",
    "version": "0.0.5",
    "changelog": {
        "0.0.5": "add name tag",
        "0.0.4": "Fix evaluation",
        "0.0.3": "Update to use MONAI 1.1.0",
        "0.0.2": "Update The Torch Vision Transform",
        "0.0.1": "initialize the model package structure"
    },
    "monai_version": "1.1.0",
    "pytorch_version": "1.13.0",
    "numpy_version": "1.21.2",
    "optional_packages_version": {
        "nibabel": "4.0.1",
        "pytorch-ignite": "0.4.9"
    },
    "name": "Pathology nuclei classification",
    "task": "Pathology Nuclei classification",
    "description": "A pre-trained model for Nuclei Classification within Haematoxylin & Eosin stained histology images",
    "authors": "MONAI team",
    "copyright": "Copyright (c) MONAI Consortium",
    "data_source": "consep_dataset.zip from https://warwick.ac.uk/fac/cross_fac/tia/data/hovernet",
    "data_type": "png",
    "image_classes": "RGB channel data, intensity scaled to [0, 1]",
    "label_classes": "single channel data",
    "pred_classes": "4 channels OneHot data, channel 0 is Other, channel 1 is Inflammatory, channel 2 is Epithelial, channel 3 is Spindle-Shaped",
    "eval_metrics": {
        "f1_score": 0.85
    },
    "intended_use": "This is an example, not to be used for diagnostic purposes",
    "references": [
        "S. Graham, Q. D. Vu, S. E. A. Raza, A. Azam, Y-W. Tsang, J. T. Kwak and N. Rajpoot. \"HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images.\" Medical Image Analysis, Sept. 2019. https://doi.org/10.1016/j.media.2019.101563"
    ],
    "network_data_format": {
        "inputs": {
            "image": {
                "type": "magnitude",
                "format": "RGB",
                "modality": "regular",
                "num_channels": 4,
                "spatial_shape": [
                    128,
                    128
                ],
                "dtype": "float32",
                "value_range": [
                    0,
                    1
                ],
                "is_patch_data": false,
                "channel_def": {
                    "0": "R",
                    "1": "G",
                    "2": "B",
                    "3": "Mask"
                }
            }
        },
        "outputs": {
            "pred": {
                "type": "probabilities",
                "format": "classes",
                "num_channels": 4,
                "spatial_shape": [
                    1,
                    4
                ],
                "dtype": "float32",
                "value_range": [
                    0,
                    1,
                    2,
                    3
                ],
                "is_patch_data": false,
                "channel_def": {
                    "0": "Other",
                    "1": "Inflammatory",
                    "2": "Epithelial",
                    "3": "Spindle-Shaped"
                }
            }
        }
    }
}