Using GANs to synthetically stain histopathological images to generate training data for automatic mitosis detection in breast tissue

Abstract

Generative adversarial networks (GANs) have been proven effective at mapping medical images from one domain to another (e.g. from CT to MRI). In this study we investigate the effectiveness of GANs at mapping images of breast tissue between histopathological stains. Breast cancer is the most common cancer in women worldwide. Counting mitotic figures in histological images of breast cancer tissue has been shown to be a reliable and independent prognostic marker. Most successful methods for automatic counting involve training deep neural networks on H&E stained slides. This training requires extensive manual annotations of mitotic figures in H&E stained slides, which suffers from a low inter-observer agreement. Manual counting in PHH3 stained slides has a much higher inter-observer agreement. In this project we aimed to train GANs to map PHH3 slides to synthetic H&E slides and vice versa. A mitosis classifier is used to quantify the quality of the synthetic images, by comparing its performance after training on synthetic images with training on real images.