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Learning domain-agnostic visual representation for computational pathology using medically-irrelevant style transfer augmentation

Study overview

This repository contains the code for learning robust and generalizable visual representation using unrealistic style transfer augmentation in digital pathology. We focus on a particular task of classifying colorectal cancer into distinct genetic subtypes called microsatellite status using H&E-stained FFPE histopathology images.

medically-irrelevant style transfer augmentation

Software Requirements

This code was developed and tested in the following settings.

OS

  • Ubuntu 18.04

GPU

  • Nvidia GeForce RTX 2080 Ti

Dependencies

  • captum (0.2.0)
  • h5py (2.9.0)
  • histomicstk (1.0.3.dev56)
  • matplotlib (3.1.0)
  • numpy: (1.18.1)
  • pandas (0.25.3)
  • pillow (7.0.0)
  • pytables (3.5.1)
  • python (3.6.10)
  • pytorch (1.4.0)
  • scikit-learn (0.21.3)
  • scipy (1.3.2)
  • seaborn (0.11.0)
  • torchvision (0.5.0)
  • tqdm (4.41.1)

Installation

  • Install Miniconda on your machine (download the distribution that comes with python3).

  • Create a conda environment with environment.yml:

conda env create -f environment.yml
  • Activate the environment:
conda activate strap

Demo

data collection

  • Prepare your own dataset following this repository.
  • Download CRC-DX-TRAIN and CRC-DX-TEST datasets from here.
  • Download the train.zip file of the Kaggle’s Painter by Numbers dataset from here.
  • Download the miniImageNet dataset from here.

prepare stylized datasets

python create_stylized_dataset.py --content-path /path/to/content_images.hdf5 \
    --style-dir /path/to/style_images --out-path /path/to/save/stylized_dataset.hdf5 \
    --alpha 1.0 --content-size 1024 --style-size 256 --save-size 256

train models

python train.py --path2hdf5 /path/to/development-dataset.hdf5 \
    --save-dir directory /path/to/save/state-dicts --experiment 'style_transfer'  

python train.py --path2hdf5 /path/to/development-dataset.hdf5 \
    --save-dir /path/to/save/state-dicts --experiment 'stain_augmentation'  

python train.py --path2hdf5 /path/to/development-dataset.hdf5 \
    --save-dir /path/to/save/state-dicts --experiment 'stain_normalization'

evaluate models

python eval.py --data-dir /path/to/CRC-DX-TEST-dataset \
    --state-dict-dir /path/to/state-dicts --experiment 'style_transfer'  

python eval.py --data-dir /path/to/CRC-DX-TEST-dataset \
    --state-dict-dir /path/to/state-dicts --experiment 'stain_augmentation'  

python eval.py --data-dir /path/to/CRC-DX-TEST-dataset \
    --state-dict-dir /path/to/state-dicts --experiment 'stain_normalization'

create low-frequency datasets

python decompose_frequency.py --data-dir /path/to/CRC-DX-TEST-dataset \
    --save-dir /path/to/save/low-frequency-datasets

evaluate models on low-frequency datasets

python eval_on_low_freq.py --data-dir /path/to/low-freq-CRC-DX-TEST-dataset \
    --state-dict-dir /path/to/state-dicts --out-dir /path/to/save/low-frequency-results  

python plot_low_freq_results.py --csv-path /path/to/low-frequency-results.csv \
    --out-dir /path/to/save/low-frequency-plots  

python integrated_gradients.py --data-dir /path/to/CRC-DX-TEST-dataset \
    --low-data-dir /path/to/low-freq-CRC-DX-TEST-dataset --state-dict-dir /path/to/state-dicts \
    --out-dir /path/to/save/integrated-gradient-plots \
    --idx 25600 --kfold 1 --radius 70 --reference 'uniform'

Note: please edit paths above.

Citation

Arxiv paper

@article{yamashita2021learning,
  title={Learning domain-agnostic visual representation for computational pathology using medically-irrelevant style transfer augmentation}, 
  author={Rikiya Yamashita and Jin Long and Snikitha Banda and Jeanne Shen and Daniel L. Rubin},
  journal={arXiv preprint arXiv:2102.01678},
  year={2021}
  }