mlsanityData to 7 Oct 2026

Surgical Workflow Recognition: from Analysis of Challenges to Architectural Study

arXiv 2203.09230 ↗First posted 17 Mar 2022cs.CVcited by 6

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Abstract (arXiv)

Algorithmic surgical workflow recognition is an ongoing research field and can be divided into laparoscopic (Internal) and operating room (External) analysis. So far many different works for the internal analysis have been proposed with the combination of a frame-level and an additional temporal model to address the temporal ambiguities between different workflow phases. For the External recognition task, Clip-level methods are in the focus of researchers targeting the local ambiguities present in the OR scene. In this work we evaluate combinations of different model architectures for the task of surgical workflow recognition to provide a fair comparison of the methods for both Internal and External analysis. We show that methods designed for the Internal analysis can be transferred to the external task with comparable performance gains for different architectures.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 24 of its 35 references that are in the map. First posted on arXiv 17 Mar 2022.

Builds on

24 of 35 references.

References · 24

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12 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 0; 2025: 2; 2026: 1.

6 papers.

  1. 2023LABRAD-OR: Lightweight Memory Scene Graphs for Accurate Bimodal Reasoning in Dynamic Operating Roomscited by 21
  2. 2023Metrics Matter in Surgical Phase Recognitioncited by 13
  3. 2023TUNeS: A Temporal U-Net with Self-Attention for Video-based Surgical Phase Recognitioncited by 6
  4. 2025Beyond Role-Based Surgical Domain Modeling: Generalizable Re-Identification in the Operating Roomcited by 6
  5. 2025TrackOR: Towards Personalized Intelligent Operating Rooms Through Robust Trackingcited by 2
  6. 2026Self-Supervised Uncalibrated Multi-View Video Anonymization in the Operating Roomcited by 0