机器人辅助食管切除术手术阶段识别模型的基准测试与增强
Benchmarking and Enhancing Surgical Phase Recognition Models for Robotic-Assisted Esophagectomy
AI总结：
面向复杂 RAMIE 手术，构建含 27 个视频的阶段识别数据集，基准评估现有模型，并提出带因果分层注意力的编码器–解码器模型以提升识别性能。
摘要：
机器人辅助微创食管切除术（RAMIE）是治疗食管癌的公认方法，与开放手术和传统微创手术相比，可为患者带来更好的预后。RAMIE 高度复杂，跨越多个解剖区域，并包含重复性阶段和非顺序性阶段转换。我们的目标是利用深度学习进行 RAMIE 中的手术阶段识别，从而为外科医生提供术中支持。为实现这一目标，我们构建了一个包含 27 个视频的新型手术阶段识别数据集。利用该数据集，我们对最先进的手术阶段识别模型进行了比较分析。为更有效地捕捉这一复杂手术的时间动态，我们开发了一种新型深度学习模型，其采用带有因果分层注意力的编码器–解码器结构，并表现出优于现有模型的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
