针对机器人食管癌手术，构建新数据集并提出更优时序识别模型。
Benchmarking and Enhancing Surgical Phase Recognition Models for Robotic-Assisted Esophagectomy
- 用因果分层注意力设计新模型，捕捉手术时序复杂性。
- 在27段视频数据上，新模型准确率显著优于现有方法。
- 适合关注手术自动化与智能辅助的医疗AI研究者。
机器人辅助微创食管切除术（RAMIE）是治疗食管癌的先进方式，相比开放手术和传统微创手术具有更好患者预后。由于该手术涉及多个解剖区域、重复性阶段及非顺序阶段转换，过程高度复杂。本文旨在利用深度学习实现RAMIE中的手术阶段识别，为术中医生提供实时支持。为此，我们构建了一个包含27个视频的新手术阶段识别数据集，并对当前主流模型进行了对比分析。为更有效建模此类复杂手术的时序动态，我们提出一种基于编码器-解码器结构的新型深度学习模型，引入因果分层注意力机制。实验表明，该模型在多项指标上优于现有方法。
原文摘要 · Abstract (English)
Robotic-assisted minimally invasive esophagectomy (RAMIE) is a recognized treatment for esophageal cancer, offering better patient outcomes compared to open surgery and traditional minimally invasive surgery. RAMIE is highly complex, spanning multiple anatomical areas and involving repetitive phases and non-sequential phase transitions. Our goal is to leverage deep learning for surgical phase recognition in RAMIE to provide intraoperative support to surgeons. To achieve this, we have developed a new surgical phase recognition dataset comprising 27 videos. Using this dataset, we conducted a comparative analysis of state-of-the-art surgical phase recognition models. To more effectively capture the temporal dynamics of this complex procedure, we developed a novel deep learning model featuring an encoder-decoder structure with causal hierarchical attention, which demonstrates superior performance compared to existing models.
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