Weakly-Supervised Surgical Phase Recognition

2023/10/26 by Roy Hirsch, Regev Cohen, Hirsch, Roy +9
Medicine · #Colorectal Cancer Screening and Detection #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG)

paper · pdf · doi:10.48550/arxiv.2310.17209

openalex publication_date 2023/10/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Related

• Learning from a tiny dataset of manual annotations: a teacher/student approach for surgical phase recognition
• Less is More: Surgical Phase Recognition with Less Annotations through Self-Supervised Pre-training of CNN-LSTM Networks
• SemiVT-Surge: Semi-Supervised Video Transformer for Surgical Phase Recognition
• Retrieval of surgical phase transitions using reinforcement learning
• Not End-to-End: Explore Multi-Stage Architecture for Online Surgical Phase Recognition