{
    "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20240725.json",
    "version": "0.5.2",
    "changelog": {
        "0.5.2": "enhance metadata with improved descriptions",
        "0.5.1": "update to huggingface hosting",
        "0.5.0": "use monai 1.4 and update large files",
        "0.4.9": "update to use monai 1.3.1",
        "0.4.8": "add load_pretrain flag for infer",
        "0.4.7": "add missing yaml lib requirement in metadata",
        "0.4.6": "add checkpoint loader for infer",
        "0.4.5": "set image_only to False",
        "0.4.4": "update the benchmark results of TensorRT",
        "0.4.3": "add support for TensorRT conversion and inference",
        "0.4.2": "update search function to match monai 1.2",
        "0.4.1": "fix the wrong GPU index issue of multi-node",
        "0.4.0": "remove error dollar symbol in readme",
        "0.3.9": "add cpu ram requirement in readme",
        "0.3.8": "add non-deterministic note",
        "0.3.7": "re-train model with updated dints implementation",
        "0.3.6": "black autofix format and add name tag",
        "0.3.5": "restructure readme to match updated template",
        "0.3.4": "correct typos",
        "0.3.3": "update learning rate and readme",
        "0.3.2": "update to use monai 1.0.1",
        "0.3.1": "fix license Copyright error",
        "0.3.0": "update license files",
        "0.2.0": "unify naming",
        "0.1.1": "fix data type issue in searching/training configurations",
        "0.1.0": "complete the model package",
        "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": {
        "fire": "0.6.0",
        "nibabel": "5.2.1",
        "pytorch-ignite": "0.4.11",
        "PyYAML": "6.0.1",
        "scikit-learn": "1.5.1",
        "tensorboard": "2.17.0"
    },
    "supported_apps": {},
    "name": "Pancreas and Tumor DiNTS Segmentation",
    "task": "Pancreas and Pancreatic Tumor Segmentation in CT Images",
    "description": "A 3D segmentation model optimized through Neural Architecture Search (DiNTS) that processes 96x96x96 pixel patches from CT scans to segment pancreas and pancreatic tumors. The model architecture was automatically discovered to balance accuracy and computational efficiency, achieving a mean Dice score of 0.62 across both structures.",
    "authors": "MONAI team",
    "copyright": "Copyright (c) MONAI Consortium",
    "data_source": "Task07_Pancreas.tar from http://medicaldecathlon.com/",
    "data_type": "nibabel",
    "image_classes": "single channel data, intensity scaled to [0, 1]",
    "label_classes": "single channel data, 1 is pancreas, 2 is pancreatic tumor, 0 is everything else",
    "pred_classes": "3 channels OneHot data, channel 1 is pancreas, channel 2 is pancreatic tumor, channel 0 is background",
    "eval_metrics": {
        "mean_dice": 0.62
    },
    "intended_use": "This is an example, not to be used for diagnostic purposes",
    "references": [
        "He, Y., Yang, D., Roth, H., Zhao, C. and Xu, D., 2021. Dints: Differentiable neural network topology search for 3d medical image segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 5841-5850)."
    ],
    "network_data_format": {
        "inputs": {
            "image": {
                "type": "image",
                "format": "hounsfield",
                "modality": "CT",
                "num_channels": 1,
                "spatial_shape": [
                    96,
                    96,
                    96
                ],
                "dtype": "float32",
                "value_range": [
                    0,
                    1
                ],
                "is_patch_data": true,
                "channel_def": {
                    "0": "image"
                }
            }
        },
        "outputs": {
            "pred": {
                "type": "image",
                "format": "segmentation",
                "num_channels": 3,
                "spatial_shape": [
                    96,
                    96,
                    96
                ],
                "dtype": "float32",
                "value_range": [
                    0,
                    1,
                    2
                ],
                "is_patch_data": true,
                "channel_def": {
                    "0": "background",
                    "1": "pancreas",
                    "2": "pancreatic tumor"
                }
            }
        }
    }
}