File size: 4,123 Bytes
54f43fd |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 |
{
"schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_hovernet_20221124.json",
"version": "0.1.3",
"changelog": {
"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": "1.1.0",
"pytorch_version": "1.13.0",
"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": 0.8293,
"PQ": 0.4936,
"F1d": 0.748
},
"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"
}
}
}
}
}
|