Surgical phase recognition is essential for analyzing procedure-specific surgical videos. While recent transformer-based architectures have advanced sequence processing capabilities, they struggle with maintaining consistency across lengthy surgical procedures. Drawing inspiration from classical hidden Markov models' finite-state interpretations, we introduce the neural finite-state machine (NFSM) module, which bridges procedural understanding with deep learning approaches. NFSM combines procedure-level understanding with neural networks through global state embeddings, attention-based dynamic transition tables, and transition-aware training and inference mechanisms for offline and online applications. When integrated into our future-aware architecture, NFSM improves video-level accuracy, phase-level precision, recall, and Jaccard indices on Cholec80 datasets by 2.3, 3.2, 3.0, and 4.8 percentage points respectively. As an add-on module to existing state-of-the-art models like Surgformer, NFSM further enhances performance, demonstrating its complementary value. Extended experiments on non-surgical datasets validate NFSM's generalizability beyond surgical domains. Comprehensive experiments demonstrate that incorporating NSFM into deep learning frameworks enables more robust and consistent phase recognition across long procedural videos.

本研究解决了外科阶段识别中针对程序特定手术视频分析的可持续性问题。作者提出了一种神经有限状态机（NFSM）模块，结合了程序级理解与深度学习，通过全局状态嵌入和动态转移表改善了长流程视频的阶段识别准确性。在Cholec80数据集上，NFSM 的引入使视频级和阶段级表现显著提升，展示了其在外科领域以及其他领域的广泛适应性和增值潜力。

用于外科阶段识别的神经有限状态机