Surgical phase recognition is a key task in computer-assisted surgery, aiming to automatically identify and categorize the different phases within a surgical procedure. Despite substantial advancements, most current approaches rely on fully supervised training, requiring expensive and time-consuming frame-level annotations. Timestamp supervision has recently emerged as a promising alternative, significantly reducing annotation costs while maintaining competitive performance. However, models trained on timestamp annotations can be negatively impacted by missing phase annotations, leading to a potential drawback in real-world scenarios. In this work, we address this issue by proposing a robust method for surgical phase recognition that can handle missing phase annotations effectively. Furthermore, we introduce the SkipTag@K annotation approach to the surgical domain, enabling a flexible balance between annotation effort and model performance. Our method achieves competitive results on two challenging datasets, demonstrating its efficacy in handling missing phase annotations and its potential for reducing annotation costs. Specifically, we achieve an accuracy of 85.1\% on the MultiBypass140 dataset using only 3 annotated frames per video, showcasing the effectiveness of our method and the potential of the SkipTag@K setup. We perform extensive experiments to validate the robustness of our method and provide valuable insights to guide future research in surgical phase recognition. Our work contributes to the advancement of surgical workflow recognition and paves the way for more efficient and reliable surgical phase recognition systems.

翻译：手术阶段识别是计算机辅助手术中的关键任务，旨在自动识别和分类手术过程中的不同阶段。尽管取得了显著进展，当前大多数方法依赖于全监督训练，需要昂贵且耗时的逐帧标注。时间戳监督最近成为一种有前景的替代方案，能在保持竞争性性能的同时显著降低标注成本。然而，基于时间戳标注训练的模型可能受到缺失阶段标注的负面影响，这在真实场景中构成潜在缺陷。在本工作中，我们通过提出一种能够有效处理缺失阶段标注的鲁棒手术阶段识别方法来解决此问题。此外，我们将SkipTag@K标注方法引入手术领域，实现了标注工作量与模型性能之间的灵活平衡。我们的方法在两个具有挑战性的数据集上取得了具有竞争力的结果，证明了其处理缺失阶段标注的有效性以及降低标注成本的潜力。具体而言，我们在MultiBypass140数据集上仅使用每个视频3个标注帧就达到了85.1\%的准确率，这展示了我们方法的有效性以及SkipTag@K设置的潜力。我们进行了大量实验以验证方法的鲁棒性，并为指导未来手术阶段识别研究提供了有价值的见解。我们的工作推动了手术工作流识别的发展，并为构建更高效可靠的手术阶段识别系统铺平了道路。