Selective Synthetic Augmentation with HistoGAN for Improved Histopathology Image Classification

TL;DR

This paper introduces HistoGAN, a conditional GAN for generating realistic histopathology image patches, and a selective augmentation framework that improves classification accuracy on limited-annotation datasets.

Contribution

The paper presents a novel HistoGAN model and a selective synthetic augmentation method that enhances histopathology image classification performance.

Findings

Significant accuracy improvements on two histopathology datasets.

Effective synthetic augmentation with quality assurance.

HistoGAN generates realistic, class-conditioned image patches.

Abstract

Histopathological analysis is the present gold standard for precancerous lesion diagnosis. The goal of automated histopathological classification from digital images requires supervised training, which requires a large number of expert annotations that can be expensive and time-consuming to collect. Meanwhile, accurate classification of image patches cropped from whole-slide images is essential for standard sliding window based histopathology slide classification methods. To mitigate these issues, we propose a carefully designed conditional GAN model, namely HistoGAN, for synthesizing realistic histopathology image patches conditioned on class labels. We also investigate a novel synthetic augmentation framework that selectively adds new synthetic image patches generated by our proposed HistoGAN, rather than expanding directly the training set with synthetic images. By selecting synthetic images based on the confidence of their assigned labels and their feature similarity to real labeled images, our framework provides quality assurance to synthetic augmentation. Our models are evaluated on two datasets: a cervical histopathology image dataset with limited annotations, and another dataset of lymph node histopathology images with metastatic cancer. Here, we show that leveraging HistoGAN generated images with selective augmentation results in significant and consistent improvements of classification performance (6.7% and 2.8% higher accuracy, respectively) for cervical histopathology and metastatic cancer datasets.