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