Self-supervised learning via cluster distance prediction for operating room context awareness.
Journal:
International journal of computer assisted radiology and surgery
PMID:
35471624
Abstract
PURPOSE: Semantic segmentation and activity classification are key components to create intelligent surgical systems able to understand and assist clinical workflow. In the operating room, semantic segmentation is at the core of creating robots aware of clinical surroundings, whereas activity classification aims at understanding OR workflow at a higher level. State-of-the-art semantic segmentation and activity recognition approaches are fully supervised, which is not scalable. Self-supervision can decrease the amount of annotated data needed.