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Related Experiment Video

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Surgical instrument-tissue interaction recognition with multi-task-attention video transformer.

Lennart Maack1, Berk Cam2, Sarah Latus2

  • 1Institute of Medical Technology and Intelligent Systems, Hamburg University of Technology, Hamburg, Germany. lennart.maack@tuhh.de.

International Journal of Computer Assisted Radiology and Surgery
|November 10, 2025
PubMed
Summary

This study shows that fine-grained temporal context significantly improves surgical instrument-tissue interaction recognition. A novel multi-task-attention module (MTAM) enhances performance by integrating spatio-temporal features for better surgical analysis.

Keywords:
Deep learningSurgical activity recognitionSurgical triplet recognitionVideo transformer

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Area of Science:

  • Computer Vision
  • Medical Robotics
  • Surgical Analytics

Background:

  • Surgical instrument-tissue interaction recognition is crucial for enhancing surgical workflow analysis, automated safety systems, and skill assessment in minimally invasive surgery.
  • Current deep learning methods often use static images or coarse temporal sampling, failing to capture rapid surgical dynamics.

Purpose of the Study:

  • To systematically investigate the impact of fine-grained temporal context on deep learning models for surgical instrument-tissue interaction recognition.
  • To evaluate the optimal temporal sampling rate for capturing surgical dynamics.

Main Methods:

  • Utilized video transformers with spatio-temporal feature extraction capabilities on curated video datasets.
  • Proposed a novel multi-task-attention module (MTAM) employing cross-attention and a gating mechanism for improved subtask communication (instrument, action, target identification).

Main Results:

  • Fine-grained temporal context demonstrated significant benefits for instrument-tissue interaction recognition, with an optimal sampling rate of 6-8 Hz identified.
  • The proposed MTAM outperformed state-of-the-art methods on CholecT45-Vid and GraSP-Vid datasets, achieving relative performance increases of 4.8% and 5.9%, respectively.

Conclusions:

  • Fine-grained temporal context is superior to static images or coarse temporal context for surgical instrument-tissue interaction recognition.
  • Leveraging cross-attention with spatio-temporal features across multiple subtasks enhances recognition performance.