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Scaling up self-supervised learning for improved surgical foundation models.

Tim J M Jaspers1, Ronald L P D de Jong2, Yiping Li2

  • 1Department of Electrical Engineering, Video Coding & Architectures, Eindhoven University of Technology, Eindhoven, The Netherlands.

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Summary
This summary is machine-generated.

SurgeNetXL, a new surgical foundation model trained on 4.7 million frames, achieves state-of-the-art performance in surgical computer vision tasks like segmentation and phase recognition.

Keywords:
Self-supervised learningSurgeNetSurgical computer visionTransfer learning

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Area of Science:

  • Computer Vision
  • Medical AI
  • Surgical Technology

Background:

  • Foundation models excel in general computer vision but are underutilized in surgical applications.
  • Existing surgical computer vision models lack broad applicability and robustness.

Purpose of the Study:

  • Introduce SurgeNetXL, a novel foundation model for surgical computer vision.
  • Establish a new benchmark for performance and generalization in surgical AI.
  • Provide insights into optimizing foundation models for surgical data.

Main Methods:

  • Trained SurgeNetXL on the largest surgical dataset to date (4.7 million video frames).
  • Evaluated performance across six datasets, four surgical procedures, and three tasks (segmentation, phase recognition, CVS classification).
  • Compared SurgeNetXL against existing surgical foundation models and ImageNet1k.

Main Results:

  • SurgeNetXL achieved top-tier performance, outperforming the previous best surgical model by up to 11.4%.
  • Demonstrated significant improvements over ImageNet1k, with gains up to 16.1% in specific tasks.
  • Established new benchmarks for semantic segmentation, surgical phase recognition, and critical view of safety classification.

Conclusions:

  • SurgeNetXL sets a new standard for surgical computer vision foundation models.
  • Findings offer a framework for scaling datasets and optimizing architectures for surgical AI.
  • The model and dataset advance generalization and robustness in data-scarce surgical scenarios.