SurgicalMamba：手术阶段实时识别新方法 SurgicalMamba: Dual-Path SSD with State Regramming for Online Surgical Phase Recognition

精读摘要

在线手术阶段识别要求对长达数小时的手术每一帧都给出预测，只能看过去的帧，且单帧成本不能随时间增长。结构化状态空间对偶（SSD）满足这一约束，但每头的标量转移既固定了状态写入位置，也固定了衰减速度——手术中反复出现的相似画面会被自己覆盖，只能靠「新旧」区分。SurgicalMamba 提出带状态重编程的双路径 SSD 方案，解决重复内容的区分与衰减速度选择问题。 Online surgical phase recognition must predict at every frame of hours-long procedures from past frames alone, at constant per-frame cost. SSD meets this constraint, but its per-head scalar transition fixes both where state is written and how fast it decays, so recurring views overwrite themselves and can only be told apart by age. SurgicalMamba proposes a dual-path SSD with state regramming to address this.