imclab / style-transfer-for-digital-pathology

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

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.

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medically-irrelevant style transfer augmentation

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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}
  }