  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.
