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A cell comparative multiple instance learning network guided by image quality assessment for cervical whole slide

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This study introduces a new AI framework for cervical cancer screening using whole slide images (WSIs). The quality-aware system improves accuracy by addressing image variations and enhancing cell feature differences.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Early cervical cancer screening is crucial for reducing mortality.
  • Artificial intelligence (AI) analysis of whole slide images (WSIs) offers automated screening potential.
  • Existing AI methods struggle with image quality variations and individual morphological differences, limiting clinical robustness.

Purpose of the Study:

  • To develop a quality-aware cervical WSI classification framework.
  • To integrate image quality assessment with pathologist-inspired cell comparison.
  • To enhance the robustness and accuracy of AI-based cervical cancer screening.

Main Methods:

  • A quality evaluation module filters unreliable image patches.
  • A cell comparison and enhancement strategy minimizes individual variability by enlarging normal-abnormal cell feature discrepancies.
  • Supervised contrastive learning and attention-based multiple instance learning guide WSI classification using patch-level quality scores.

Main Results:

  • The proposed framework demonstrates superior performance in real-world scenarios.
  • The method significantly outperforms state-of-the-art approaches.
  • Achieved a 1.93% average overall accuracy improvement on 2,434 WSIs from five institutions.

Conclusions:

  • The quality-aware framework enhances AI-based cervical cancer screening accuracy and robustness.
  • Integrating image quality assessment and cell-level feature enhancement is effective.
  • This approach shows promise for reliable, large-scale automated cervical cancer screening in clinical settings.