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The automatic detection of frames containing polyps from a colonoscopy videosequence is an important first step for a fully automated colonoscopy analysistool. Typically, such detection system is built using a large annotated dataset of frames with and without polyps, which is expensive to be obtained. Inthis paper, we introduce a new system that detects frames containing polyps asanomalies from a distribution of frames from exams that do not contain anypolyps. The system is trained using a one-class training set consisting ofcolonoscopy frames without polyps -- such training set is considerably lessexpensive to obtain, compared to the 2-class data set mentioned above. Duringinference, the system is only able to reconstruct frames without polyps, andwhen it tries to reconstruct a frame with polyp, it automatically removes(i.e., photoshop) it from the frame -- the difference between the input andreconstructed frames is used to detect frames with polyps. We name our proposedmodel as anomaly detection generative adversarial network (ADGAN), comprising adual GAN with two generators and two discriminators. We show that our proposedapproach achieves the state-of-the-art result on this data set, compared withrecently proposed anomaly detection systems.

Unsupervised Dual Adversarial Learning for Anomaly Detection in Colonoscopy Video Frames