Holistic Surgical Phase Recognition with Hierarchical Input Dependent State Space Models

TL;DR

This paper introduces a hierarchical state space model for surgical phase recognition that efficiently processes full-length videos by capturing local and global dynamics, outperforming transformer-based methods.

Contribution

A novel hierarchical input-dependent state space model that enables efficient, comprehensive surgical workflow analysis on full-length videos with improved accuracy.

Findings

Outperforms state-of-the-art methods by up to 12.9% on Heichole dataset.

Effectively captures local and global temporal dynamics.

Enables decision making on full-length surgical videos.

Abstract

Surgical workflow analysis is essential in robot-assisted surgeries, yet the long duration of such procedures poses significant challenges for comprehensive video analysis. Recent approaches have predominantly relied on transformer models; however, their quadratic attention mechanism restricts efficient processing of lengthy surgical videos. In this paper, we propose a novel hierarchical input-dependent state space model that leverages the linear scaling property of state space models to enable decision making on full-length videos while capturing both local and global dynamics. Our framework incorporates a temporally consistent visual feature extractor, which appends a state space model head to a visual feature extractor to propagate temporal information. The proposed model consists of two key modules: a local-aggregation state space model block that effectively captures intricate local dynamics, and a global-relation state space model block that models temporal dependencies across the entire video. The model is trained using a hybrid discrete-continuous supervision strategy, where both signals of discrete phase labels and continuous phase progresses are propagated through the network. Experiments have shown that our method outperforms the current state-of-the-art methods by a large margin (+2.8% on Cholec80, +4.3% on MICCAI2016, and +12.9% on Heichole datasets). Code will be publicly available after paper acceptance.