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{
"schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json",
"version": "0.1.4",
"changelog": {
"0.1.4": "fix the wrong GPU index issue of multi-node",
"0.1.3": "remove error dollar symbol in readme",
"0.1.2": "add RAM warning",
"0.1.1": "enable deterministic eval and inference",
"0.1.0": "Update deterministic results",
"0.0.9": "Update README Formatting",
"0.0.8": "enable deterministic training",
"0.0.7": "update benchmark on A100",
"0.0.6": "adapt to BundleWorkflow interface",
"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": "1.2.0",
"pytorch_version": "1.13.1",
"numpy_version": "1.22.2",
"optional_packages_version": {
"nibabel": "4.0.1",
"pytorch-ignite": "0.4.9",
"torchvision": "0.14.1"
},
"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": 0.852
},
"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"
}
}
}
}
}
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