发表机构
Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对眼科手术时间动作定位中长程依赖建模的计算瓶颈，提出基于双向状态空间对偶性与权重共享扫描的OphBiWSSD框架，以线性复杂度实现高精度定位，在OphNet上超越基线6.8%和6.66%。
AI 中文摘要
眼科手术中的高频手术操作需要高保真度的时间建模，然而，对于基于注意力的架构而言，刻画长程手术流程依赖关系在计算上仍然难以承受。现有模型通常需要进行激进的时间下采样，这损害了对细粒度动作边界和器械-组织交互的检测。为解决这些可扩展性限制，我们提出了OphBiWSSD，一个利用双向状态空间对偶性重新构建手术时间动作定位的框架。通过采用结合前后手术上下文的权重共享选择性扫描机制，我们的方法以线性复杂度实现了非因果时间线索的全局综合。这种精简架构非常适合捕捉眼科工作流程中存在的双向依赖关系，有效弥合了局部边界精度与长程手术流程上下文之间的差距，而无需承担传统Transformer的二次方内存开销。在OphNet基准上的大量实验表明，OphBiWSSD达到了最先进的时间定位性能，在阶段和操作上的平均精度分别为44.42%和43.08%，分别超过基线6.80%和6.66%。实证验证表明，我们的方法确保了精确的时间定位，并为在临床环境中部署手术智能系统提供了一条计算上可行的途径。代码可在该https URL公开获取。
英文摘要
High-frequency surgical maneuvers in ophthalmology necessitate high-fidelity temporal modeling, yet characterizing long-range procedural dependencies remains computationally prohibitive for attention-based architectures. Existing models often require aggressive temporal downsampling, which compromises the detection of fine-grained action boundaries and instrument-tissue interactions. To address these scalability constraints, we present OphBiWSSD, a framework that reformulates surgical temporal action localization leveraging Bidirectional State Space Duality. By employing a weight-tied selective scan mechanism that incorporates both preceding and succeeding surgical contexts, our approach facilitates the global synthesis of non-causal temporal cues with linear complexity. This streamlined architecture is well-suited to capture the bidirectional dependencies present in ophthalmic workflows, effectively bridging the gap between local boundary precision and long-range procedural context without incurring the quadratic memory overhead of traditional Transformers. Extensive experiments on the OphNet benchmark demonstrate that OphBiWSSD achieves state-of-the-art temporal localization performance, with mean Average Precisions of 44.42% on phases and 43.08% on operations, surpassing the baselines by 6.80% and 6.66%, respectively. Empirical validation indicates that our approach ensures precise temporal localization and offers a computationally viable pathway for deploying surgical intelligence systems in clinical environments. The code is publicly available at https://github.com/yo3nglau/OphBiWSSD.
CommentsSubmitted to IEEE Transactions on Image Processing. Under review
