Weakly-Supervised Surgical Phase Recognition
Roy Hirsch Regev Cohen Mathilde Caron Tomer Golany Daniel Freedman Ehud Rivlin
A key element of computer-assisted surgery systems is phase recognition of
surgical videos. Existing phase recognition algorithms require frame-wise
annotation of a large number of videos, which is time and money consuming. In
this work we join concepts of graph segmentation with self-supervised learning
to derive a random-walk solution for per-frame phase prediction. Furthermore,
we utilize within our method two forms of weak supervision: sparse timestamps
or few-shot learning. The proposed algorithm enjoys low complexity and can
operate in lowdata regimes. We validate our method by running experiments with
the public Cholec80 dataset of laparoscopic cholecystectomy videos,
demonstrating promising performance in multiple setups.