Weakly-Supervised Surgical Phase Recognition
This work addresses the need for efficient computer-assisted surgery systems by reducing the time and cost of frame-wise annotations, though it appears incremental as it builds on existing concepts.
The paper tackles the problem of surgical phase recognition in videos by proposing a method that uses graph segmentation and self-supervised learning with weak supervision, such as sparse timestamps or few-shot learning, to reduce annotation costs. It demonstrates promising performance on the Cholec80 dataset, though no concrete numbers are provided.
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.