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arXiv 2107.04810cs.CVcs.AI
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非端到端：探索用于在线手术阶段识别的多阶段架构

Not End-to-End: Explore Multi-Stage Architecture for Online Surgical Phase Recognition

Peking University（北京大学）

Fangqiu Yi, Tingting Jiang

备注：
Not accepted by M2CAI2021
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摘要：

手术阶段识别对于计算机辅助手术系统特别感兴趣，其中目标是预测在手术视频的每个帧处发生什么阶段。具有多级架构的网络已广泛应用于许多具有丰富模式的计算机视觉任务中，其中预测器级首先输出初始预测，并且附加的细化级对初始预测进行操作以执行进一步的细化。现有的工作表明，手术视频内容是有序的，包含丰富的时间模式，使多阶段的架构非常适合手术阶段识别任务。然而，我们观察到，当简单地将多阶段架构应用于手术阶段识别任务时，端到端的训练方式会使细化能力达不到其期望。为了解决这个问题，我们提出了一种新的非端到端的训练策略，并探讨了不同的设计多阶段的架构，手术阶段识别任务。对于非端到端的训练策略，细化阶段分别用提出的两种类型的扰动序列进行训练。同时，我们评估了三种不同的细化模型的选择，以表明我们的分析和解决方案是鲁棒的选择特定的多阶段模型。我们在两个公共基准上进行实验，M2CAI16工作流挑战和Cholec80数据集。结果表明，使用我们的策略训练的多阶段架构大大提高了当前最先进的单阶段模型的性能。代码可在\url{https://github.com/ChinaYi/casual_tcn}上获得。

英文摘要：

Surgical phase recognition is of particular interest to computer assisted surgery systems, in which the goal is to predict what phase is occurring at each frame for a surgery video. Networks with multi-stage architecture have been widely applied in many computer vision tasks with rich patterns, where a predictor stage first outputs initial predictions and an additional refinement stage operates on the initial predictions to perform further refinement. Existing works show that surgical video contents are well ordered and contain rich temporal patterns, making the multi-stage architecture well suited for the surgical phase recognition task. However, we observe that when simply applying the multi-stage architecture to the surgical phase recognition task, the end-to-end training manner will make the refinement ability fall short of its wishes. To address the problem, we propose a new non end-to-end training strategy and explore different designs of multi-stage architecture for surgical phase recognition task. For the non end-to-end training strategy, the refinement stage is trained separately with proposed two types of disturbed sequences. Meanwhile, we evaluate three different choices of refinement models to show that our analysis and solution are robust to the choices of specific multi-stage models. We conduct experiments on two public benchmarks, the M2CAI16 Workflow Challenge, and the Cholec80 dataset. Results show that multi-stage architecture trained with our strategy largely boosts the performance of the current state-of-the-art single-stage model. Code is available at \url{https://github.com/ChinaYi/casual_tcn}.

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。

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