MAL

Prepare your dataset

The dataset consists of two parts: 1 Original DCM file, 2 The tumor annotation area corresponding to each DCM file (JSON format).

For example:

data_root = '/path/to/your/data'

train.json
{
    "images": [
        {
            "id": 1,
            "file_name": "0001.DCM (should be a relative path for data_root)",
            "width": 384,
            "height": 384
        }
    ],
    "annotations": [
        {
            "id": 1,
            "image_id": 1,
            "patient_id": "patient_id from dcm file, e.g. gqFv9tThLVuxG0SJKGfaWQ==",
            "position": "plane, e.g. axial",
            "category_id": 1,
            "bbox": [
                175.1849710982659,
                229.1907514450867,
                66.76300578034684,
                55.49132947976878
            ]
        }
    ],
    "categories": [
        {
            "id": 1,
            "name": "Malignant",
            "supercategory": "defect"
        },
        {
            "id": 0,
            "name": "Benign",
            "supercategory": "defect"
        }
    ]
}

The specific example of JSON file is as follows, the absolute path of 0001.DCM is /path/to/your/data/0001.DCM

{
    "images": [
        {
            "id": 1,
            "file_name": "0001.DCM",
            "width": 384,
            "height": 384
        }
    ],
    "annotations": [
        {
            "id": 1,
            "image_id": 1,
            "patient_id": "gqFv9tThLVuxG0SJKGfaWQ==",
            "position": "axial",
            "category_id": 1,
            "bbox": [
                175.1849710982659,
                229.1907514450867,
                66.76300578034684,
                55.49132947976878
            ]
        }
    ],
    "categories": [
        {
            "id": 1,
            "name": "Malignant",
            "supercategory": "defect"
        },
        {
            "id": 0,
            "name": "Benign",
            "supercategory": "defect"
        }
    ]
}

you should modify the config/base/comm_config.py:

train_json = 'to your train.json'
test_json = 'to your test.json'
data_root = 'to your data root'

The code will automatically construct the bipartite graph of DCM data in JSON according to patients and their planes, and carry out corresponding training and testing.

Install the necessary libraries

pip install -r requirements.txt

Train

CUDA_VISIBLE_DEVICES=0,1,2,3 sh dist_train.sh config/11_mg_axi_sag_multibranch_pcloss_tcloss.py 4

The checkpoint will be saved ./work_dirs/11_mg_axi_sag_multibranch_pcloss_tcloss/

Test

CUDA_VISIBLE_DEVICES=0,1,2,3 sh dist_test.sh work_dirs/11_mg_axi_sag_multibranch_pcloss_tcloss/11_mg_axi_sag_multibranch_pcloss_tcloss.py  work_dirs/11_mg_axi_sag_multibranch_pcloss_tcloss/latest.pth 4 --metrics accuracy