  Following the successful debut of polyp detection and characterization, more
advanced automation tools are being developed for colonoscopy. The new
automation tasks, such as quality metrics or report generation, require
understanding of the procedure flow that includes activities, events,
anatomical landmarks, etc. In this work we present a method for automatic
semantic parsing of colonoscopy videos. The method uses a novel DL multi-label
temporal segmentation model trained in supervised and unsupervised regimes. We
evaluate the accuracy of the method on a test set of over 300 annotated
colonoscopy videos, and use ablation to explore the relative importance of
various method's components.
