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Summary

This study introduces a federated learning (FL) framework for instrument segmentation using diverse, imperfect medical datasets. The proposed FL approach outperforms centralized learning, addressing data silos and privacy concerns effectively.

Keywords:
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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Machine Learning

Background:

  • Instrument segmentation is crucial in medical imaging, but large-scale centralized datasets are hindered by data silos and privacy concerns.
  • Local medical datasets often contain diverse and imperfect annotations, including scarce, noisy, or scribble labels.
  • Federated learning (FL) offers a solution for training models on distributed data without centralizing it.

Purpose of the Study:

  • To investigate the potential of federated learning for instrument segmentation using distributed datasets with imperfect annotations.
  • To develop a practical FL framework addressing the challenges of scarce, noisy, and scribble annotations in medical imaging.
  • To establish a benchmark for FL-based instrument segmentation in real-world complex scenarios.

Main Methods:

  • A novel federated learning framework was proposed to handle instrument segmentation tasks.
  • The framework was designed to learn from distributed datasets with various types of imperfect annotations (scarce, noisy, scribble).
  • Performance was evaluated against centralized learning approaches under different imperfect annotation settings.

Main Results:

  • The proposed federated learning framework demonstrated superior performance compared to centralized learning methods.
  • The approach effectively managed and learned from datasets with diverse and imperfect annotations.
  • The study established a new benchmark for instrument segmentation in a federated, imperfect annotation setting.

Conclusions:

  • Federated learning is a viable and effective paradigm for instrument segmentation, especially when dealing with distributed and imperfect datasets.
  • The developed FL framework offers a practical solution for real-world medical imaging challenges, surpassing traditional centralized methods.
  • This work lays the groundwork for future research in federated instrument segmentation, considering more complex and diverse annotation scenarios.