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  3. Privacy-aware Continual Self-supervised Learning On Multi-window Chest Computed Tomography For Domain-shift Robustness.
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  3. Privacy-aware Continual Self-supervised Learning On Multi-window Chest Computed Tomography For Domain-shift Robustness.

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Privacy-Aware Continual Self-Supervised Learning on Multi-Window Chest Computed Tomography for Domain-Shift

Ren Tasai1, Guang Li2, Ren Togo3

  • 1Graduate School of Information Science and Technology, Hokkaido University, N-14, W-9, Kita-ku, Sapporo 060-0814, Hokkaido, Japan.

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View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a privacy-preserving continual self-supervised learning framework for chest CT images. It effectively handles domain shifts from different window settings, improving medical image diagnosis models.

Keywords:
chest CT imagecontinual self-supervised learningfeature distillationlatent replayself-supervised learning

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Medical image analysis faces challenges due to limited annotated data and domain shifts, particularly in chest CT scans with varying window settings.
  • Existing continual self-supervised learning (CSSL) methods often require reusing past data, which is impractical due to data privacy concerns.

Purpose of the Study:

  • To develop a novel CSSL framework for robust feature learning from multi-window chest CT images while ensuring data privacy.
  • To address catastrophic forgetting and domain shifts in medical image diagnosis through innovative continual pretraining strategies.

Main Methods:

  • Implemented a latent replay-based mechanism within CSSL to mitigate forgetting during continual pretraining and maintain data privacy.
  • Introduced a feature distillation technique combining Wasserstein distance-based knowledge distillation and batch-knowledge ensemble for robust representation learning.
  • Utilized unlabeled chest CT images across different window settings for continual pretraining.

    • Main Results:

    • The proposed framework demonstrated superior performance in learning meaningful, domain-shift-robust representations from chest CT images.
    • Effectively captured relationships between previously learned knowledge and new information across training stages, mitigating domain shift impacts.
    • Validated the approach on multi-window chest CT data, outperforming existing methods.

    Conclusions:

    • The novel CSSL framework offers a privacy-preserving solution for robust medical image analysis, particularly for chest CT scans with varying window settings.
    • The integration of latent replay and feature distillation enhances model generalization and resilience to domain shifts.
    • This approach advances the development of reliable AI models in healthcare by addressing data scarcity and privacy constraints.