Colonoscopic Image Synthesis For Polyp Detector Enhancement Via Gan And Adversarial Training

Abstract

Computer-aided polyp detection system powered by deep neural networks has achieved high performance but also suffers from data insufficiency. To address this problem, recent researches focus on synthesizing new colonoscopic images by Generative Adversarial Network (GAN). However, the synthesized images follow the same distribution as that of the training dataset, which limits the performance of the detectors re-trained on it. Recent studies show that adversarial examples can expand the data distribution and thus adversarial training can effectively improve the robustness of deep neural networks. Inspired by these two factors, this paper proposes a data augmentation framework to directly produce false negative colonoscopic images via GAN and the adversarial attack. The synthesized polyps are natural and experiments on three popular detectors show that compared with using GAN alone, producing false negative images by the adversarial attack can further improve the performance of the re-trained detectors.