KPIs2024

KPIs challenge 2024

Training example docker

Get our docker image for Training (Task 1)

    docker pull hrlblab333/kpis:1.0

Run the docker

    # you need to specify the input directory
    export input_dir=your_input_directory
    # you need to specify the output directory
    export output_dir=your_output_directory
    # make that directory
    mkdir $input_dir
    mkdir $output_dir
    #run the docker
    docker run --rm -v $input_dir:/input/:ro -v $output_dir:/output --gpus all -it hrlblab333/kpis:1.0

Validation example docker

Get our docker image for Validation (Task 1)

    docker pull hrlblab333/kpis:validation_patch   

    # you need to specify the input, output directory, and the model path
    docker run --rm -v $input_dir:/input/:ro -v $model:/model/:ro -v $output_dir:/output --gpus all -it hrlblab333/kpis:validation_patch

File structure (task1)

The directory for both training and validation needs to have the following structure:

input_dir
    └── 56NX
        └── case1
            └── img
                └── patch1.jpg
                └── patch2.jpg
                ...
            └── mask
                └── patch1_mask.jpg
                └── patch2_mask.jpg
                ...
        └── case2
        └── case3
        ...
    └── DN
    └── NEP25
    └── normal

Get our docker image for Validation (Task 2)

    docker pull hrlblab333/kpis:validation_slide

    # you need to specify the input, output directory, and the model path
    # you can specify the patch_save, and patch_mask_save directories to save the middle product for further assessment.
    docker run --rm -v $input_dir:/input_slide/:ro -v $model:/model/:ro -v $output_dir:/output_slide -v $patch_save:/input_patch -v $patch_mask_save:/output_patch --gpus all -it hrlblab333/kpis:validation_slide

File structure (task2)

The directory for validation, task2 need to have the following structure:

input_dir
    └── 56NX
        └── img
            └── case1.tiff
            └── case2.tiff
            ...
        └── mask
            └── case1_mask.tiff
            └── case2_mask.tiff
            ...
    └── DN
    └── NEP25
    └── normal

The segmentation output should be at 20x magnification.