Self-Supervised Polyp Re-Identification in Colonoscopy
Yotam Intrator Natalie Aizenberg Amir Livne Ehud Rivlin Roman Goldenberg
Computer-aided polyp detection (CADe) is becoming a standard, integral part
of any modern colonoscopy system. A typical colonoscopy CADe detects a polyp in
a single frame and does not track it through the video sequence. Yet, many
downstream tasks including polyp characterization (CADx), quality metrics,
automatic reporting, require aggregating polyp data from multiple frames. In
this work we propose a robust long term polyp tracking method based on
re-identification by visual appearance. Our solution uses an attention-based
self-supervised ML model, specifically designed to leverage the temporal nature
of video input. We quantitatively evaluate method's performance and demonstrate
its value for the CADx task.