File size: 3,669 Bytes
650e244 5fbf6fb 650e244 5fbf6fb fd8c5bc 5c16b7b f0039cf 39d9ce9 3c8069c 8b5b5dc 6ecf3f0 8008a76 75e9798 9f733ac e982bf7 e42f548 650e244 5fbf6fb 8008a76 650e244 8008a76 650e244 75e9798 650e244 |
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 |
{
"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 usage with CachDataset",
"0.1.1": "deterministic retrain benchmark and add link",
"0.1.0": "fix mgpu finalize issue",
"0.0.9": "Update README Formatting",
"0.0.8": "enable deterministic training",
"0.0.7": "Update with figure links",
"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 nuclick annotation",
"task": "Pathology Nuclick annotation",
"description": "A pre-trained model for segmenting nuclei cells with user clicks/interactions",
"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"
}
}
}
}
}
|