Weakly-Supervised Surgical Phase Recognition

• URL: http://arxiv.org/abs/2310.17209v1
• Date: Thu, 26 Oct 2023 07:54:47 GMT
• Title: Weakly-Supervised Surgical Phase Recognition
• Authors: Roy Hirsch, Regev Cohen, Mathilde Caron, Tomer Golany, Daniel Freedman, Ehud Rivlin
• Abstract summary: In this work we join concepts of graph segmentation with self-supervised learning to derive a random-walk solution for per-frame phase prediction. We validate our method by running experiments with the public Cholec80 dataset of laparoscopic cholecystectomy videos.
• Score: 19.27227976291303
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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