Consistency Loss for Improved Colonoscopy Landmark Detection with Vision Transformers

Colonoscopy is a procedure used to examine the colon and rectum for colorectal cancer or other abnormalities including polyps or diverticula. Apart from the actual diagnosis, manually processing the snapshots taken during the colonoscopy procedure (for medical record keeping) consumes a large amount of the clinician's time. This can be automated through post-procedural machine learning based algorithms which classify anatomical landmarks in the colon. In this work, we have developed a pipeline for training vision-transformers for identifying anatomical landmarks, including appendiceal orifice, ileocecal valve/cecum landmark and rectum retroflection. To increase the accuracy of the model, we utilize a hybrid approach that combines algorithm-level and data-level techniques. We introduce a consistency loss to enhance model immunity to label inconsistencies, as well as a semantic non-landmark sampling technique aimed at increasing focus on colonic findings. For training and testing our pipeline, we have annotated 307 colonoscopy videos and 2363 snapshots with the assistance of several medical experts for enhanced reliability. The algorithm identifies landmarks with an accuracy of 92% on the test dataset.