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Workshop: 4th Workshop on Self-Supervised Learning: Theory and Practice

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.

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