Self-Supervised Polyp Re-Identification in Colonoscopy

• URL: http://arxiv.org/abs/2306.08591v1
• Date: Wed, 14 Jun 2023 15:53:54 GMT
• Title: Self-Supervised Polyp Re-Identification in Colonoscopy
• Authors: Yotam Intrator, Natalie Aizenberg, Amir Livne, Ehud Rivlin, Roman Goldenberg
• Abstract summary: 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.
• Score: 1.9678816712224196
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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