# Self-Supervised Polyp Re-Identification in Colonoscopy

arXiv: https://arxiv.org/abs/2306.08591 · HTML: https://mlsanity.com/paper/2306.08591/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 24 references that are in the map.
- First posted on arXiv 14 Jun 2023.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [ByteTrack: Multi-Object Tracking by Associating Every Detection Box](https://mlsanity.com/paper/2110.06864/) (2021)
- [In Defense of the Triplet Loss for Person Re-Identification](https://mlsanity.com/paper/1703.07737/) (2017)
- [Large Batch Optimization for Deep Learning: Training BERT in 76 minutes](https://mlsanity.com/paper/1904.00962/) (2019)
- [Understanding the Behaviour of Contrastive Loss](https://mlsanity.com/paper/2012.09740/) (2020)
- [Revisiting Temporal Modeling for Video-based Person ReID](https://mlsanity.com/paper/1805.02104/) (2018)
- [Dense Interaction Learning for Video-based Person Re-identification](https://mlsanity.com/paper/2103.09013/) (2021)
- [Not 3D Re-ID: a Simple Single Stream 2D Convolution for Robust Video Re-identification](https://mlsanity.com/paper/2008.06318/) (2020)

## Built on by (most cited first, 8 of 8)

- [Self-Supervised Learning for Endoscopic Video Analysis](https://mlsanity.com/paper/2308.12394/) (2023)
- [Frontiers in Intelligent Colonoscopy](https://mlsanity.com/paper/2410.17241/) (2024)
- [EndoFinder: Online Image Retrieval for Explainable Colorectal Polyp Diagnosis](https://mlsanity.com/paper/2407.11401/) (2024)
- [Towards Polyp Counting In Full-Procedure Colonoscopy Videos](https://mlsanity.com/paper/2502.10054/) (2025)
- [Temporally-Aware Supervised Contrastive Learning for Polyp Counting in Colonoscopy](https://mlsanity.com/paper/2507.02493/) (2025)
- [Predicting Generalization of AI Colonoscopy Models to Unseen Data](https://mlsanity.com/paper/2403.09920/) (2024)
- [Focus-to-Perceive Representation Learning: A Cognition-Inspired Hierarchical Framework for Endoscopic Video Analysis](https://mlsanity.com/paper/2603.25778/) (2026)
- [Contrastive Learning under Noisy Temporal Self-Supervision for Colonoscopy Videos](https://mlsanity.com/paper/2605.12320/) (2026)

## Abstract (arXiv)

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.