mlsanityData to 14 Oct 2026

Metrics Matter in Surgical Phase Recognition

arXiv 2305.13961 ↗First posted 23 May 2023cs.CVcited by 14

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Abstract (arXiv)

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.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 43 of its 95 references that are in the map. First posted on arXiv 23 May 2023.

Builds on

43 of 95 references.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 3; 2024: 2; 2025: 7; 2026: 2.

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  1. 2025SurgLaVi: Large-Scale Hierarchical Dataset for Surgical Vision-Language Representation Learningcited by 16
  2. 2023TUNeS: A Temporal U-Net with Self-Attention for Video-based Surgical Phase Recognitioncited by 6
  3. 2023SF-TMN: SlowFast Temporal Modeling Network for Surgical Phase Recognitioncited by 5
  4. 2024Neural Finite-State Machines for Surgical Phase Recognitioncited by 5
  5. 2025CAT-SG: A Large Dynamic Scene Graph Dataset for Fine-Grained Understanding of Cataract Surgerycited by 4
  6. 2025Federated EndoViT: Pretraining Vision Transformers via Federated Learning on Endoscopic Image Collectionscited by 3
  7. 2024Benchmarking and Enhancing Surgical Phase Recognition Models for Robotic-Assisted Esophagectomycited by 2
  8. 2025Efficient Frame Extraction: A Novel Approach Through Frame Similarity and Surgical Tool Tracking for Video Segmentationcited by 2
  9. 2025MoSFormer: Augmenting Temporal Context with Memory of Surgery for Surgical Phase Recognitioncited by 2
  10. 2025SemiVT-Surge: Semi-Supervised Video Transformer for Surgical Phase Recognitioncited by 2
  11. 2023Surgical Phase and Instrument Recognition: How to identify appropriate Dataset Splitscited by 1
  12. 2025ReSW-VL: Representation Learning for Surgical Workflow Analysis Using Vision-Language Modelcited by 0
2 more
  1. 2026SurgicalMamba: Dual-Path SSD with State Regramming for Online Surgical Phase Recognitioncited by 0
  2. 2026HTT-Net: Hierarchical Text-guided Transition Modeling for Surgical Video Phase Recognitioncited by 0