Weakly-Supervised Surgical Phase Recognition

TL;DR

This paper introduces a low-complexity, weakly-supervised method for surgical phase recognition in videos, combining graph segmentation and self-supervised learning to reduce annotation effort and perform well with limited data.

Contribution

It presents a novel approach that integrates graph segmentation with self-supervised learning and weak supervision for efficient surgical phase recognition.

Findings

Effective in low-data regimes

Promising performance on Cholec80 dataset

Utilizes sparse timestamps and few-shot learning

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.