| { | |
| "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20240725.json", | |
| "version": "0.2.2", | |
| "changelog": { | |
| "0.2.2": "enhance metadata with improved descriptions", | |
| "0.2.1": "update to huggingface hosting", | |
| "0.2.0": "update issue for IgniteInfo", | |
| "0.1.9": "update tensorrt benchmark results", | |
| "0.1.8": "enable tensorrt", | |
| "0.1.7": "update to use monai 1.3.1", | |
| "0.1.6": "set image_only to False", | |
| "0.1.5": "add support for TensorRT conversion and inference", | |
| "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 warning", | |
| "0.1.1": "enable deterministic eval and inference", | |
| "0.1.0": "Update deterministic results", | |
| "0.0.9": "Update README Formatting", | |
| "0.0.8": "enable deterministic training", | |
| "0.0.7": "update benchmark on A100", | |
| "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": "1.4.0", | |
| "pytorch_version": "2.4.0", | |
| "numpy_version": "1.24.4", | |
| "required_packages_version": { | |
| "nibabel": "5.2.1", | |
| "pytorch-ignite": "0.4.11", | |
| "torchvision": "0.19.0", | |
| "scipy": "1.13.1", | |
| "scikit-learn": "1.5.1", | |
| "tensorboard": "2.17.0", | |
| "scikit-image": "0.23.2" | |
| }, | |
| "supported_apps": {}, | |
| "name": "Pathology Nuclei Classification", | |
| "task": "Multi-class Nuclei Classification in H&E Histology Images", | |
| "description": "A deep learning model based on the HoVer-Net architecture that classifies nuclei in H&E-stained histology images. The model processes 128x128 pixel RGB images with nuclei masks and classifies four distinct cell types: inflammatory, epithelial, spindle-shaped, and other nuclei", | |
| "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": "4 channels OneHot data, channel 0 is Other, channel 1 is Inflammatory, channel 2 is Epithelial, channel 3 is Spindle-Shaped", | |
| "eval_metrics": { | |
| "f1_score": 0.852 | |
| }, | |
| "intended_use": "This is an example, not to be used for diagnostic purposes", | |
| "references": [ | |
| "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" | |
| ], | |
| "network_data_format": { | |
| "inputs": { | |
| "image": { | |
| "type": "magnitude", | |
| "format": "RGB", | |
| "modality": "regular", | |
| "num_channels": 4, | |
| "spatial_shape": [ | |
| 128, | |
| 128 | |
| ], | |
| "dtype": "float32", | |
| "value_range": [ | |
| 0, | |
| 1 | |
| ], | |
| "is_patch_data": false, | |
| "channel_def": { | |
| "0": "R", | |
| "1": "G", | |
| "2": "B", | |
| "3": "Mask" | |
| } | |
| } | |
| }, | |
| "outputs": { | |
| "pred": { | |
| "type": "probabilities", | |
| "format": "classes", | |
| "num_channels": 4, | |
| "spatial_shape": [ | |
| 1, | |
| 4 | |
| ], | |
| "dtype": "float32", | |
| "value_range": [ | |
| 0, | |
| 1, | |
| 2, | |
| 3 | |
| ], | |
| "is_patch_data": false, | |
| "channel_def": { | |
| "0": "Other", | |
| "1": "Inflammatory", | |
| "2": "Epithelial", | |
| "3": "Spindle-Shaped" | |
| } | |
| } | |
| } | |
| } | |
| } | |