File size: 4,249 Bytes
3ceb398 5659a77 3ceb398 5659a77 3ceb398 5659a77 3ceb398 |
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 |
{
"schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20240725.json",
"version": "0.6.4",
"changelog": {
"0.6.4": "enhance metadata with improved descriptions",
"0.6.3": "update to huggingface hosting",
"0.6.2": "enhance readme for nccl timout issue",
"0.6.1": "fix multi-gpu issue",
"0.6.0": "use monai 1.4 and update large files",
"0.5.9": "update to use monai 1.3.1",
"0.5.8": "update readme to add memory warning",
"0.5.7": "update channel_def in metadata",
"0.5.6": "fix the wrong GPU index issue of multi-node",
"0.5.5": "modify mgpu logging level",
"0.5.4": "retrain using an internal pretrained ResNet18",
"0.5.3": "make the training bundle deterministic",
"0.5.2": "update TensorRT descriptions",
"0.5.1": "update the TensorRT part in the README file",
"0.5.0": "add the command of executing inference with TensorRT models",
"0.4.9": "adapt to BundleWorkflow interface",
"0.4.8": "update the readme file with TensorRT convert",
"0.4.7": "add name tag",
"0.4.6": "modify dataset key name",
"0.4.5": "update model weights and perfomance metrics",
"0.4.4": "restructure readme to match updated template",
"0.4.3": "fix wrong figure url",
"0.4.2": "update metadata with new metrics",
"0.4.1": "Fix inference print logger and froc",
"0.4.0": "add lesion FROC calculation and wsi_reader",
"0.3.3": "update to use monai 1.0.1",
"0.3.2": "enhance readme on commands example",
"0.3.1": "fix license Copyright error",
"0.3.0": "update license files",
"0.2.0": "unify naming",
"0.1.1": "fix location variable name change",
"0.1.0": "initialize release of the bundle"
},
"monai_version": "1.4.0",
"pytorch_version": "2.4.0",
"numpy_version": "1.24.4",
"required_packages_version": {
"cucim-cu12": "24.6.0",
"pandas": "2.2.1",
"torchvision": "0.19.0",
"pytorch-ignite": "0.4.11",
"tensorboard": "2.17.0"
},
"supported_apps": {},
"name": "Pathology Tumor Detection",
"task": "Metastatic Tissue Detection in Whole-Slide Pathology Images",
"description": "A deep learning model for detecting metastatic tissue in whole-slide pathology images. The model processes 224x224 pixel RGB patches and provides probability scores for metastasis detection. Trained on the Camelyon16 dataset",
"authors": "MONAI team",
"copyright": "Copyright (c) MONAI Consortium",
"data_source": "Camelyon dataset",
"data_type": "tiff",
"image_classes": "RGB image with intensity between 0 and 255",
"label_classes": "binary labels for each patch",
"pred_classes": "scalar probability",
"eval_metrics": {
"accuracy": 0.9,
"froc": 0.72
},
"intended_use": "This is an example, not to be used for diagnostic purposes",
"references": [
""
],
"network_data_format": {
"inputs": {
"image": {
"type": "image",
"format": "magnitude",
"num_channels": 3,
"spatial_shape": [
224,
224
],
"dtype": "float32",
"value_range": [
0,
255
],
"is_patch_data": true,
"channel_def": {
"0": "R",
"1": "G",
"2": "B"
}
}
},
"outputs": {
"pred": {
"type": "probability",
"format": "classification",
"num_channels": 1,
"spatial_shape": [
1,
1
],
"dtype": "float32",
"is_patch_data": true,
"value_range": [
0,
1
],
"channel_def": {
"0": "metastasis"
}
}
}
}
}
|