BrainMRIAD/sample_config.json at main · theotimefd/BrainMRIAD · GitHub

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{
    "description": "DDPM Unsupervised Anomaly Detection inference with simplex noise (3D slice by slice)",
    "input_folder": "your_input_folder/",
    "output_folder": "your_output_folder/",
    "gpu_device": 1,
    "sequence": "ADC",
    "save_segmentations": true,
    "segmentation_threshold":0.04,
    "already_preprocessed": true,
    "save_preprocessed_images": true,
    "use_post_processing": true,
    "replace_files": true,
    "image_size": 128,
    "nb_inferences":1,
    "models_folder":"models/",
    "noise": {
        "type": "simplex",
        "schedule":"cosine",
        "simplex_octaves":6,
        "simplex_persistence":0.8,
        "simplex_frequency":64,
        "normalize": false,
        "num_timesteps_full_noise": 1000,
        "num_timesteps_inference": 130
    },
    "post_processing": {
        "median_filter_size": 5,
        "erosion_dilation_steps": 4,
        "fill_holes": true
    },
    "num_workers": 8,
    "spatial_dims": 2,
    "slice_indexes_start":22,
    "slice_indexes_end": 94,
    "val_transforms": {
        "_target_": "monai.transforms.Compose",
        "transforms": [
            {
                "_target_": "monai.transforms.LoadImage", "image_only": true
            },
            {
                "_target_": "monai.transforms.EnsureChannelFirst"
            },
            {
                "_target_": "monai.transforms.ResizeWithPadOrCrop", "spatial_size":["@image_size", "@image_size", "@image_size"]
            },
            {
                "_target_": "utils.custom_transforms.ScaleIntensityFromHistogramPeak", "target_value": 200.0, "bins": 1000
            },
            {
                "_target_": "monai.transforms.ScaleIntensityRange", "a_min":0.0, "a_max":700.0, "b_min":0.0, "b_max":1.0, "clip":true
            },
            {
                "_target_": "utils.custom_transforms.SetBackgroundToZero"
            }
        ]
    },
    "network_def": {
        "_target_": "monai.networks.nets.DiffusionModelUNet",
        "spatial_dims": "@spatial_dims",
        "in_channels": 1,
        "out_channels": 1,
        "channels":[32, 64, 64, 64],
        "attention_levels":[false, true, true, true],
        "num_head_channels":8,
        "use_flash_attention":true
    },
    "thor": {
        "enable": false,
        "nb_timesteps_harmonization": 7
    }
}