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Metrics Matter in Surgical Phase Recognition
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Metrics Matter in Surgical Phase Recognition
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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.
Forward citations
Cited by 5 Pith papers
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SurgicalMamba: Dual-Path SSD with State Regramming for Online Surgical Phase Recognition
SurgicalMamba adapts Mamba2 with dual-path SSD, intensity-modulated stepping, and state regramming to reach state-of-the-art online accuracy on seven surgical phase recognition benchmarks while keeping per-frame cost ...
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SurgicalMamba: Dual-Path SSD with State Regramming for Online Surgical Phase Recognition
SurgicalMamba achieves SOTA online accuracy on surgical phase recognition benchmarks by adding dual-path SSD, intensity-modulated stepping, and state regramming to Mamba2 while keeping per-frame cost O(d).
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Current validation practice undermines surgical AI development
A consensus-based catalog of 18 validation pitfalls, with evidence that common practices understate uncertainty, hide failures, and flip algorithm rankings in surgical video AI.
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HTT-Net: Hierarchical Text-guided Transition Modeling for Surgical Video Phase Recognition
A hierarchical text-guided network that constructs and calibrates phase segments improves surgical phase recognition, setting a high Jaccard on Cholec80 and reporting large gains on a private LCRS-100 benchmark.
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Current validation practice undermines surgical AI development
A multi-stage Delphi consensus with 92 experts catalogs widespread validation pitfalls in surgical AI video analysis across data, metrics, and reporting, supported by a systematic review and empirical experiments.
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