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{
"schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json",
"version": "0.0.1",
"changelog": {
"0.0.1": "initialize the model package structure"
},
"monai_version": "1.0.1",
"pytorch_version": "1.13.0",
"numpy_version": "1.21.2",
"optional_packages_version": {
"nibabel": "4.0.1",
"pytorch-ignite": "0.4.9"
},
"task": "Pathology Nuclick segmentation",
"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": "1 channel data, with value 1 as nuclei and 0 as background",
"eval_metrics": {
"mean_dice": 0.85
},
"intended_use": "This is an example, not to be used for diagnostic purposes",
"references": [
"Koohbanani, Navid Alemi, et al. \"NuClick: A Deep Learning Framework for Interactive Segmentation of Microscopy Images.\" https://arxiv.org/abs/2005.14511",
"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",
"NuClick PyTorch Implementation, https://github.com/mostafajahanifar/nuclick_torch"
],
"network_data_format": {
"inputs": {
"image": {
"type": "png",
"format": "RGB",
"modality": "regular",
"num_channels": 5,
"spatial_shape": [
128,
128
],
"dtype": "float32",
"value_range": [
0,
1
],
"is_patch_data": false,
"channel_def": {
"0": "R",
"1": "G",
"2": "B",
"3": "+ve Signal",
"4": "-ve Signal"
}
}
},
"outputs": {
"pred": {
"type": "image",
"format": "segmentation",
"num_channels": 1,
"spatial_shape": [
128,
128
],
"dtype": "float32",
"value_range": [
0,
1
],
"is_patch_data": false,
"channel_def": {
"0": "Nuclei"
}
}
}
}
}
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