Abstract
The automatic detection of frames containing polyps from a colonoscopy video
sequence is an important first step for a fully automated colonoscopy analysis
tool. Typically, such detection system is built using a large annotated data
set of frames with and without polyps, which is expensive to be obtained. In
this paper, we introduce a new system that detects frames containing polyps as
anomalies from a distribution of frames from exams that do not contain any
polyps. The system is trained using a one-class training set consisting of
colonoscopy frames without polyps -- such training set is considerably less
expensive to obtain, compared to the 2-class data set mentioned above. During
inference, the system is only able to reconstruct frames without polyps, and
when it tries to reconstruct a frame with polyp, it automatically removes
(i.e., photoshop) it from the frame -- the difference between the input and
reconstructed frames is used to detect frames with polyps. We name our proposed
model as anomaly detection generative adversarial network (ADGAN), comprising a
dual GAN with two generators and two discriminators. We show that our proposed
approach achieves the state-of-the-art result on this data set, compared with
recently proposed anomaly detection systems.