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Related Experiment Video

Updated: Apr 26, 2026

A Novel Dual-Modal Deep Learning Approach for Real-Time Removal of Hepatic Fluorescence in Indocyanine Green-Guided Laparoscopic Cholecystectomy
09:21

A Novel Dual-Modal Deep Learning Approach for Real-Time Removal of Hepatic Fluorescence in Indocyanine Green-Guided Laparoscopic Cholecystectomy

Published on: April 17, 2026

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Few-shot learning for surgical phase recognition: Performance and generalization in cholecystectomy.

Flakë Bajraktari1, Robert Asmußen1, Giuliano A Giacoppo1

  • 1Institute of Medical Device Technology, University of Stuttgart, Pfaffenwaldring 9, 70569, Stuttgart, Germany.

Computer Methods and Programs in Biomedicine
|April 24, 2026
PubMed
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Few-shot learning (FSL) shows promise for automated surgical phase recognition, achieving high accuracy with minimal data. Domain-specific training is key for optimal performance in surgical workflow analysis.

Area of Science:

  • Artificial Intelligence in Medicine
  • Computer Vision for Surgery
  • Machine Learning for Healthcare

Background:

  • Automated surgical phase recognition is vital for advancing digital surgery and enhancing surgical assistance systems.
  • Traditional deep learning methods struggle with limited annotated surgical data due to privacy and scarcity.
  • Few-shot learning (FSL) offers a potential solution to overcome data limitations in surgical phase recognition.

Purpose of the Study:

  • To explore the efficacy of few-shot learning (FSL) for automated surgical phase recognition.
  • To adapt a transformer-based FSL model for recognizing phases in cholecystectomy videos.
  • To evaluate the model's performance in domain-specific and cross-domain settings with minimal labeled data.

Main Methods:

Keywords:
Deep learningFew-shot learningGeneralization capabilitySurgical phase recognition

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Last Updated: Apr 26, 2026

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  • Adapted a transformer-based few-shot learning (FSL) model for action recognition to surgical phase recognition.
  • Utilized the Cholec80 dataset comprising cholecystectomy surgery videos.
  • Evaluated performance across three experimental splits, including domain-specific and cross-domain scenarios.

    • Main Results:

    • Achieved test accuracies of 89.0% (Split 1), 75.4% (Split 2), and 49.1% (Split 3).
    • Demonstrated strong applicability of FSL to surgical data, even with few labeled examples per phase.
    • Highlighted the critical importance of domain-specific training for optimal performance.

    Conclusions:

    • Few-shot learning (FSL) is a feasible approach for surgical phase recognition in low-label and transfer learning settings.
    • FSL provides a promising direction for surgical workflow analysis when annotation resources are constrained.
    • Further research can build upon this foundation to develop more robust AI-driven surgical tools.