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"schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_hovernet_20221124.json",
"version": "0.2.1",
"changelog": {
"0.2.1": "fix the wrong GPU index issue of multi-node",
"0.2.0": "Update README for how to download dataset",
"0.1.9": "add RAM warning",
"0.1.8": "Update README for pretrained weights and save metrics in evaluate",
"0.1.7": "Update README Formatting",
"0.1.6": "add non-deterministic note",
"0.1.5": "update benchmark on A100",
"0.1.4": "adapt to BundleWorkflow interface",
"0.1.3": "add name tag",
"0.1.2": "update the workflow figure",
"0.1.1": "update to use monai 1.1.0",
"0.1.0": "complete the model package"
},
"monai_version": "1.2.0",
"pytorch_version": "1.13.1",
"numpy_version": "1.22.2",
"optional_packages_version": {
"scikit-image": "0.19.3",
"scipy": "1.8.1",
"tqdm": "4.64.1",
"pillow": "9.0.1"
},
"name": "Nuclear segmentation and classification",
"task": "Nuclear segmentation and classification",
"description": "A simultaneous segmentation and classification of nuclei within multitissue histology images based on CoNSeP data",
"authors": "MONAI team",
"copyright": "Copyright (c) MONAI Consortium",
"data_source": "https://warwick.ac.uk/fac/cross_fac/tia/data/hovernet/",
"data_type": "numpy",
"image_classes": "RGB image with intensity between 0 and 255",
"label_classes": "a dictionary contains binary nuclear segmentation, hover map and pixel-level classification",
"pred_classes": "a dictionary contains scalar probability for binary nuclear segmentation, hover map and pixel-level classification",
"eval_metrics": {
"Binary Dice": 0.8291
},
"intended_use": "This is an example, not to be used for diagnostic purposes",
"references": [
"Simon Graham. 'HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images.' Medical Image Analysis, 2019. https://arxiv.org/abs/1812.06499"
],
"network_data_format": {
"inputs": {
"image": {
"type": "image",
"format": "magnitude",
"num_channels": 3,
"spatial_shape": [
"256",
"256"
],
"dtype": "float32",
"value_range": [
0,
255
],
"is_patch_data": true,
"channel_def": {
"0": "image"
}
}
},
"outputs": {
"nucleus_prediction": {
"type": "probability",
"format": "segmentation",
"num_channels": 3,
"spatial_shape": [
"164",
"164"
],
"dtype": "float32",
"value_range": [
0,
1
],
"is_patch_data": true,
"channel_def": {
"0": "background",
"1": "nuclei"
}
},
"horizontal_vertical": {
"type": "probability",
"format": "regression",
"num_channels": 2,
"spatial_shape": [
"164",
"164"
],
"dtype": "float32",
"value_range": [
0,
1
],
"is_patch_data": true,
"channel_def": {
"0": "horizontal distances map",
"1": "vertical distances map"
}
},
"type_prediction": {
"type": "probability",
"format": "classification",
"num_channels": 2,
"spatial_shape": [
"164",
"164"
],
"dtype": "float32",
"value_range": [
0,
1
],
"is_patch_data": true,
"channel_def": {
"0": "background",
"1": "type of nucleus for each pixel"
}
}
}
}
}
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