Note: Requires Python 3.8. For more information regarding setting up marugoto on your system and file management for your project, see documentation.

まるごと—A Toolbox for Building Deep Learning Workflows

Our workflow uses a DL approach which performs self-supervised feature extraction with attMIL workflow. This approach addresses a weakly supervised classification problem in which the objective is to predict a slide label from a collection of individual tiles. Some studies have reported a performance gain of the self-supervised-learning attMIL approach compared to the classical approach. Wang et al. trained a ResNet-50 on 32000 WSIs from TCGA via the RetCCL self-supervised learning algorithm. We used pre-trained architectures to extract 2048 features (“Wang-attMIL”) per tile.

Feature extraction

Download best_ckpt.pth from latest Xiyue Wang: https://drive.google.com/drive/folders/1AhstAFVqtTqxeS9WlBpU41BV08LYFUnL

python -m marugoto.extract.xiyue_wang \
    --checkpoint-path ~/Downloads/best_ckpt.pth \
    --outdir ~/TCGA_features/TCGA-CRC-DX-features/xiyue-wang \
    /mnt/TCGA_BLOCKS/TCGA-CRC-DX-BLOCKS/*

Attention-Based Multiple Instance Learning

Cross-validate a model on pre-extracted features

python -m marugoto.mil crossval \
    --clini-table tcga-crc-dx/TCGA-CRC-DX_CLINI.xlsx \
    --slide-csv tcga-crc-dx/TCGA-CRC-DX_SLIDE.csv \
    --feature-dir tcga-crc-dx/features_norm_macenko_h5 \
    --target-label isMSIH \
    --output-path output/path \
    --n-splits 5

Train a single model on pre-extracted features

python -m marugoto.mil train \
    --clini-table tcga-crc-dx/TCGA-CRC-DX_CLINI.xlsx \
    --slide-csv tcga-crc-dx/TCGA-CRC-DX_SLIDE.csv \
    --feature-dir tcga-crc-dx/features_norm_macenko_h5 \
    --target-label isMSIH \
    --output-path output/path

Deploy a model on pre-extracted features from another cohort

python -m marugoto.mil deploy \
    --clini_table tcga-crc-dx/TCGA-CRC-DX_CLINI.xlsx \
    --slide-csv tcga-crc-dx/TCGA-CRC-DX_SLIDE.csv \
    --feature-dir tcga-crc-dx/features_norm_macenko_h5 \
    --target_label isMSIH \
    --model-path training-dir/export.pkl \
    --output_path output/path

Calculate Statistics for Categorical Deployments

python -m marugoto.stats.categorical \
    deployment/path/fold-*/patient-preds.csv \
    --outpath output/path \
    --target_label isMSIH

Plot ROC Curve

python -m marugoto.visualizations.roc \
    deployment/path/fold-*/patient-preds.csv \
    --outpath output/path \
    --target-label isMSIH \
    --true-label MSIH \
    --clini-table tcga-crc-dx/TCGA-CRC-DX_CLINI.xlsx \ (optional: subgroup analysis) 
    --subgroup-label 'PRETREATED' (optional: subgroup analysis) 

Plot Precision Recall Curve

python -m marugoto.visualizations.prc \
    deployment/path/fold-*/patient-preds.csv \
    --outpath output/path \
    --target-label isMSIH \
    --true-label MSIH

Advice

• default batch size is 64 patients, consider to adapt for smaller cohorts

Running Marugoto in a Container

Marugoto can be conveniently run in a podman container. To do so, use the marugoto-container.sh convenience script. Training a MIL model can be done as follows:

./marugoto-container.sh \
    marugoto.mil train \
        --clini-table /workdir/TCGA-CRC-DX_CLINI.xlsx \
        --slide-csv /workdir/TCGA-CRC-DX_SLIDE.csv \
        --feature-dir /workdir/features_norm_macenko_h5 \
        --target-label isMSIH \
        --output-path /results

For more information on how to run podman containers, please refer to the podman documentation.