Colonoscopy plays a crucial role in the diagnosis and prognosis of various gastrointestinal diseases. Due to the challenges of collecting large-scale high-quality ground truth annotations for colonoscopy images, and more generally medical images, we explore using self-supervised features from vision transformers in three challenging tasks for colonoscopy images. Our results indicate that image-level features learned from DINO models achieve image classification performance comparable to fully supervised models, and patch-level features contain rich semantic information for object detection. Furthermore, we demonstrate that self-supervised features combined with unsupervised segmentation can be used to discover multiple clinically relevant structures in a fully unsupervised manner, demonstrating the tremendous potential of applying these methods in medical image analysis.
@article{arxiv.2312.12599,
title = {Unsupervised Segmentation of Colonoscopy Images},
author = {Heming Yao and Jérôme Lüscher and Benjamin Gutierrez Becker and Josep Arús-Pous and Tommaso Biancalani and Amelie Bigorgne and David Richmond},
journal= {arXiv preprint arXiv:2312.12599},
year = {2023}
}