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{
"schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20240725.json",
"version": "0.6.10",
"changelog": {
"0.6.10": "enhance metadata with improved descriptions and intended use",
"0.6.9": "update to huggingface hosting and fix missing dependencies",
"0.6.8": "update issue for IgniteInfo",
"0.6.7": "use monai 1.4 and update large files",
"0.6.6": "update to use monai 1.3.1",
"0.6.5": "remove notes for trt_export in readme",
"0.6.4": "add notes for trt_export in readme",
"0.6.3": "add load_pretrain flag for infer",
"0.6.2": "add checkpoint loader for infer",
"0.6.1": "fix format error",
"0.6.0": "remove meta_dict usage",
"0.5.9": "use monai 1.2.0",
"0.5.8": "update TRT memory requirement in readme",
"0.5.7": "add dataset dir example",
"0.5.6": "add the ONNX-TensorRT way of model conversion",
"0.5.5": "update retrained validation results and training curve",
"0.5.4": "add non-deterministic note",
"0.5.3": "adapt to BundleWorkflow interface",
"0.5.2": "black autofix format and add name tag",
"0.5.1": "modify dataset key name",
"0.5.0": "use detection inferer",
"0.4.5": "fixed some small changes with formatting in readme",
"0.4.4": "add data resource to readme",
"0.4.3": "update val patch size to avoid warning in monai 1.0.1",
"0.4.2": "update to use monai 1.0.1",
"0.4.1": "fix license Copyright error",
"0.4.0": "add support for raw images",
"0.3.0": "update license files",
"0.2.0": "unify naming",
"0.1.1": "add reference for LIDC dataset",
"0.1.0": "complete the model package"
},
"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",
"tensorboard": "2.17.0"
},
"supported_apps": {},
"name": "Lung Nodule CT Detection",
"task": "3D Pulmonary Nodule Detection in CT Scans",
"description": "A 3D detection model for identifying pulmonary nodules in CT scans. The model processes variable-sized patches and outputs detection boxes with classification scores. Trained on the LUNA16 challenge dataset, it provides automated screening capabilities for pulmonary nodule detection in chest CT examinations.",
"authors": "MONAI team",
"copyright": "Copyright (c) MONAI Consortium",
"data_source": "https://luna16.grand-challenge.org/Home/",
"data_type": "nibabel",
"image_classes": "1 channel data, CT at 0.703125 x 0.703125 x 1.25 mm",
"label_classes": "dict data, containing Nx6 box and Nx1 classification labels.",
"pred_classes": "dict data, containing Nx6 box, Nx1 classification labels, Nx1 classification scores.",
"eval_metrics": {
"mAP_IoU_0.10_0.50_0.05_MaxDet_100": 0.852,
"AP_IoU_0.10_MaxDet_100": 0.858,
"mAR_IoU_0.10_0.50_0.05_MaxDet_100": 0.998,
"AR_IoU_0.10_MaxDet_100": 1.0
},
"intended_use": "This is an example, not to be used for diagnostic purposes",
"references": [
"Lin, Tsung-Yi, et al. 'Focal loss for dense object detection. ICCV 2017"
],
"network_data_format": {
"inputs": {
"image": {
"type": "image",
"format": "magnitude",
"modality": "CT",
"num_channels": 1,
"spatial_shape": [
"16*n",
"16*n",
"8*n"
],
"dtype": "float16",
"value_range": [
0,
1
],
"is_patch_data": true,
"channel_def": {
"0": "image"
}
}
},
"outputs": {
"pred": {
"type": "object",
"format": "dict",
"dtype": "float16",
"num_channels": 1,
"spatial_shape": [
"n",
"n",
"n"
],
"value_range": [
-10000,
10000
]
}
}
}
}
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