| { |
| "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" |
| } |
| } |
| } |
| } |
| } |
|
|
