  We present an image translation approach to generate augmented data for
mitigating data imbalances in a dataset of histopathology images of colorectal
polyps, adenomatous tumors that can lead to colorectal cancer if left
untreated. By applying cycle-consistent generative adversarial networks
(CycleGANs) to a source domain of normal colonic mucosa images, we generate
synthetic colorectal polyp images that belong to diagnostically less common
polyp classes. Generated images maintain the general structure of their source
image but exhibit adenomatous features that can be enhanced with our proposed
filtration module, called Path-Rank-Filter. We evaluate the quality of
generated images through Turing tests with four gastrointestinal pathologists,
finding that at least two of the four pathologists could not identify generated
images at a statistically significant level. Finally, we demonstrate that using
CycleGAN-generated images to augment training data improves the AUC of a
convolutional neural network for detecting sessile serrated adenomas by over
10%, suggesting that our approach might warrant further research for other
histopathology image classification tasks.
