HealthyGAN

This repository provides the official PyTorch implementation of HealthyGAN.

Paper

HealthyGAN: Learning from Unannotated Medical Images to Detect Anomalies Associated with Human Disease

Md Mahfuzur Rahman Siddiquee^1,2, Jay Shah^1,2, Teresa Wu^1,2, Catherine Chong^2,3, Todd Schwedt^2,3, and Baoxin Li^1,2

¹Arizona State University; ²ASU-Mayo Center for Innovative Imaging; ³Mayo Clinic
Simulation and Synthesis in Medical Imaging (SASHIMI), 2022

Paper (Preprint | Camera Ready) | Presentation Slides | Poster

Abstract

Automated anomaly detection from medical images, such as MRIs and X-rays, can significantly reduce human effort in disease diagnosis. Owing to the complexity of modeling anomalies and the high cost of manual annotation by domain experts (e.g., radiologists), a typical technique in the current medical imaging literature has focused on deriving diagnostic models from healthy subjects only, assuming the model will detect the images from patients as outliers. However, in many real-world scenarios, unannotated datasets with a mix of both healthy and diseased individuals are abundant. Therefore, this paper poses the research question of how to improve unsupervised anomaly detection by utilizing (1) an unannotated set of mixed images, in addition to (2) the set of healthy images as being used in the literature. To answer the question, we propose HealthyGAN, a novel one-directional image-to-image translation method, which learns to translate the images from the mixed dataset to only healthy images. Being one-directional, HealthyGAN relaxes the requirement of cycle-consistency of existing unpaired image-to-image translation methods, which is unattainable with mixed unannotated data. Once the translation is learned, we generate a difference map for any given image by subtracting its translated output. Regions of significant responses in the difference map correspond to potential anomalies (if any). Our HealthyGAN outperforms the conventional state-of-the-art methods by significant margins on two publicly available datasets: COVID-19 and NIH ChestX-ray14, and one institutional dataset collected from Mayo Clinic.

Usage

0. Cloning the repository

$ git clone https://github.com/mahfuzmohammad/HealthyGAN.git
$ cd HealthyGAN/

1. Creating python environment

$ conda create -n healthygan python=3.9
$ conda activate healthygan
$ conda install scikit-learn scikit-image -c anaconda
$ pip install tqdm pandas
$ pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu116
$ pip install tensorflow

2. Downloading the dataset

• Covid: https://github.com/lindawangg/COVID-Net Put the train and test folder under datasets/covid

3. Training

• Covid: bash train_covid.sh

4. Testing

• Covid: bash test_covid.sh

5. Testing using pretrained models

• Covid: bash test_covid_pretrained.sh