B2Q-Net: Bidirectional Branch Query Network for Surgical Phase Recognition.

Abstract

Surgical phase recognition (SPR) is essential for surgical workflow analysis and provides immediate guidance during procedures. Existing methods aggregate frame-level information into a global representation and treat the task as frame-wise classification. However, this pipeline lacks a feedback mechanism for integrating historical information into local temporal modeling. To address this limitation, we propose the Bidirectional Branch Query Network (B2Q-Net), which reformulates the SPR task as the bidirectional query between phase-level features and frame-level features. B2Q-Net incorporates historical information during the initialization of phase queries. This enables bidirectional information flow during iterative refinement of two-level feature maps between phases and frames. Furthermore, we introduce a dual-scale selector (DSS) to generate high-quality phase queries for the current video clip. These phase queries retrieve historical information from the proposed state space query (SSQ) module, which uses learnable tokens as the historical state space to preserve historical information. Extensive evaluations on three datasets demonstrate that B2Q-Net consistently outperforms state-of-the-art methods in recognition accuracy while achieving an inference speed of 106 fps. The B2Q-Net code is available at https://github.com/vsislab/B2Q-Net.