  Colonoscopy is the most widely used medical technique for preventing
Colorectal Cancer, by detecting and removing polyps before they become
malignant. Recent studies show that around one quarter of the existing polyps
are routinely missed. While some of these do appear in the endoscopist's field
of view, others are missed due to a partial coverage of the colon. The task of
detecting and marking unseen regions of the colon has been addressed in recent
work, where the common approach is based on dense 3D reconstruction, which
proves to be challenging due to lack of 3D ground truth and periods with poor
visual content. In this paper we propose a novel and complementary method to
detect deficient local coverage in real-time for video segments where a
reliable 3D reconstruction is impossible. Our method aims to identify skips
along the colon caused by a drifted position of the endoscope during poor
visibility time intervals. The proposed solution consists of two phases. During
the first, time segments with good visibility of the colon and gaps between
them are identified. During the second phase, a trained model operates on each
gap, answering the question: Do you observe the same scene before and after the
gap? If the answer is negative, the endoscopist is alerted and can be directed
to the appropriate area in real-time. The second phase model is trained using a
contrastive loss based on auto-generated examples. Our method evaluation on a
dataset of 250 procedures annotated by trained physicians provides sensitivity
of 0.75 with specificity of 0.9.
