Unsupervised Segmentation of Colonoscopy Images
Heming Yao Jérôme Lüscher Benjamin Gutierrez Becker Josep Arús-Pous Tommaso Biancalani Amelie Bigorgne David Richmond
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.