A Deep-Learning Framework for Ovarian Cancer Subtype Classification Using Whole Slide Images.

Journal: Studies in health technology and informatics
Published Date: May 15, 2025

Abstract

Ovarian cancer, a leading cause of cancer-related deaths among women, comprises distinct subtypes each requiring different treatment approaches. This paper presents a deep-learning framework for classifying ovarian cancer subtypes using Whole Slide Imaging (WSI). Our method contains three stages: image tiling, feature extraction, and multi-instance learning. Our approach is trained and validated on a public dataset from 80 distinct patients, achieving up to 89,8% accuracy with a notable improvement in computational efficiency. The results demonstrate the potential of our framework to augment diagnostic precision in clinical settings, offering a scalable solution for the accurate classification of ovarian cancer subtypes.

Authors

  • Chenyang Wang
    Burning Rock Biotech, Guangzhou, China.
  • Qiufeng Yi
    Department of Mechanical Engineering, University of Birmingham, Edgbaston, Birmingham, UK.
  • Ali Aflakian
    Department of Mechanical Engineering, University of Birmingham, Edgbaston, Birmingham, UK.
  • Jiaqi Ye
    Department of Clinical laboratory, The Eighth Affiliated Hospital, Sun Yat-sen University, 3025 Shennan Middle Road, Shenzhen, Guangdong, 518000, China.
  • Theodoros Arvanitis
    Institute of Cancer and Genomic Sciences, School of Medical and Dental Sciences, University of Birmingham, Birmingham, UK.
  • Karl D Dearn
    School of Engineering, University of Birmingham, Edgbaston, Birmingham, UK.
  • Amir Hajiyavand
    Department of Mechanical Engineering, University of Birmingham, Edgbaston, Birmingham, UK.

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