SeLa-MIL: Developing an instance-level classifier via weakly-supervised self-training for whole slide image classification.

Journal: Computer methods and programs in biomedicine
Published Date: Jan 27, 2025

Abstract

BACKGROUND AND OBJECTIVE: Pathology image classification is crucial in clinical cancer diagnosis and computer-aided diagnosis. Whole Slide Image (WSI) classification is often framed as a multiple instance learning (MIL) problem due to the high cost of detailed patch-level annotations. Existing MIL methods primarily focus on bag-level classification, often overlooking critical instance-level information, which results in suboptimal outcomes. This paper proposes a novel semi-supervised learning approach, SeLa-MIL, which leverages both labeled and unlabeled instances to improve instance and bag classification, particularly in hard positive instances near the decision boundary.

Authors

  • Yingfan Ma
    Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai 200032, China.
  • Mingzhi Yuan
    Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai 200032, China.
  • Ao Shen
    Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai 200032, China.
  • Xiaoyuan Luo
    Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China.
  • Bohan An
    Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, 200032, China; Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Fudan University, Shanghai, 200032, China.
  • Xinrong Chen
    Academy for Engineering & Technology, Fudan University, Shanghai 200433, China.
  • Manning Wang
    Digital Medical Research Center, Fudan University, Shanghai, China.

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