Metrics Matter in Surgical Phase Recognition
원문 초록
Surgical phase recognition is a basic component for different context-aware applications in computer- and robot-assisted surgery. In recent years, several methods for automatic surgical phase recognition have been proposed, showing promising results. However, a meaningful comparison of these methods is difficult due to differences in the evaluation process and incomplete reporting of evaluation details. In particular, the details of metric computation can vary widely between different studies. To raise awareness of potential inconsistencies, this paper summarizes common deviations in the evaluation of phase recognition algorithms on the Cholec80 benchmark. In addition, a structured overview of previously reported evaluation results on Cholec80 is provided, taking known differences in evaluation protocols into account. Greater attention to evaluation details could help achieve more consistent and comparable results on the surgical phase recognition task, leading to more reliable conclusions about advancements in the field and, finally, translation into clinical practice.
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Surg-3M: A Dataset and Foundation Model for Perception in Surgical Settings
EndoMamba: An Efficient Foundation Model for Endoscopic Videos via Hierarchical Pre-training
Dual Invariance Self-training for Reliable Semi-supervised Surgical Phase Recognition
SurgPLAN++: Universal Surgical Phase Localization Network for Online and Offline Inference
SPRMamba: Surgical Phase Recognition for Endoscopic Submucosal Dissection with Mamba
DACAT: Dual-stream Adaptive Clip-aware Time Modeling for Robust Online Surgical Phase Recognition
토론
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