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pathology_tumor_detection

monai
medical
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pathology_tumor_detection
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{
    "imports": [
        "$import glob",
        "$import os"
    ],
    "bundle_root": ".",
    "output_dir": "$os.path.join(@bundle_root, 'eval')",
    "dataset_dir": "/workspace/data/medical/pathology",
    "testing_file": "$os.path.join(@bundle_root, 'testing.csv')",
    "wsi_reader": "cuCIM",
    "patch_size": [
        224,
        224
    ],
    "number_intensity_ch": 3,
    "device": "$torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')",
    "network_def": {
        "_target_": "TorchVisionFCModel",
        "model_name": "resnet18",
        "num_classes": 1,
        "use_conv": true,
        "pretrained": true
    },
    "network": "$@network_def.to(@device)",
    "preprocessing": {
        "_target_": "Compose",
        "transforms": [
            {
                "_target_": "CastToTyped",
                "keys": "image",
                "dtype": "float32"
            },
            {
                "_target_": "ScaleIntensityRanged",
                "keys": "image",
                "a_min": 0.0,
                "a_max": 255.0,
                "b_min": -1.0,
                "b_max": 1.0
            },
            {
                "_target_": "ToTensord",
                "keys": "image"
            }
        ]
    },
    "datalist": {
        "_target_": "CSVDataset",
        "src": "@testing_file",
        "kwargs_read_csv": {
            "names": [
                "image"
            ],
            "header": null
        },
        "transform": {
            "_target_": "Lambdad",
            "keys": "image",
            "func": "$lambda x: os.path.join(@dataset_dir, 'testing/images', x + '.tif')"
        }
    },
    "dataset": {
        "_target_": "MaskedPatchWSIDataset",
        "data": "@datalist",
        "mask_level": 6,
        "patch_size": "@patch_size",
        "transform": "@preprocessing",
        "reader": "@wsi_reader"
    },
    "dataloader": {
        "_target_": "DataLoader",
        "dataset": "@dataset",
        "batch_size": 400,
        "shuffle": false,
        "num_workers": 8
    },
    "inferer": {
        "_target_": "SimpleInferer"
    },
    "postprocessing": {
        "_target_": "Compose",
        "transforms": [
            {
                "_target_": "EnsureTyped",
                "keys": "pred"
            },
            {
                "_target_": "Activationsd",
                "keys": "pred",
                "sigmoid": true
            },
            {
                "_target_": "ToNumpyd",
                "keys": "pred"
            }
        ]
    },
    "handlers": [
        {
            "_target_": "CheckpointLoader",
            "load_path": "$@bundle_root + '/models/model.pt'",
            "load_dict": {
                "model": "@network"
            }
        },
        {
            "_target_": "StatsHandler",
            "tag_name": "progress",
            "iteration_print_logger": "$lambda engine: print(f'image: \"{engine.state.batch[\"image\"].meta[\"name\"][0]}\", iter: {engine.state.iteration}/{engine.state.epoch_length}') if engine.state.iteration % 100 == 0 else None",
            "output_transform": "$lambda x: None"
        },
        {
            "_target_": "monai.handlers.ProbMapProducer",
            "output_dir": "@output_dir"
        }
    ],
    "evaluator": {
        "_target_": "SupervisedEvaluator",
        "device": "@device",
        "val_data_loader": "@dataloader",
        "network": "@network",
        "inferer": "@inferer",
        "postprocessing": "@postprocessing",
        "val_handlers": "@handlers",
        "amp": true,
        "decollate": false
    },
    "run": [
        "[email protected]()"
    ]
}