CAMIL: channel attention-based multiple instance learning for whole slide image classification.

Journal: Bioinformatics (Oxford, England)
PMID: 39820310

Abstract

MOTIVATION: The classification task based on whole-slide images (WSIs) is a classic problem in computational pathology. Multiple instance learning (MIL) provides a robust framework for analyzing whole slide images with slide-level labels at gigapixel resolution. However, existing MIL models typically focus on modeling the relationships between instances while neglecting the variability across the channel dimensions of instances, which prevents the model from fully capturing critical information in the channel dimension.

Authors

  • Jinyang Mao
    School of Mathematics and Statistics, Fuzhou University, Fuzhou 350108, China.
  • Junlin Xu
    School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, Hubei 430065, China.
  • Xianfang Tang
    School of Computer Science and Artificial Intelligence, Wuhan Textile University, Wuhan 430200, China.
  • Yongjin Liu
    School of Mathematics and Statistics, Fuzhou University, Fuzhou 350108, China.
  • Heaven Zhao
    Geneis Beijing Co., Ltd, Beijing 100102, China.
  • Geng Tian
    Department of Sciences, Genesis (Beijing) Co. Ltd., Beijing, China.
  • Jialiang Yang
    Department of Sciences, Genesis (Beijing) Co. Ltd., Beijing, China.

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