Masked hypergraph learning for weakly supervised histopathology whole slide image classification.

Journal: Computer methods and programs in biomedicine
Published Date: May 23, 2024

Abstract

BACKGROUND AND OBJECTIVES: Graph neural network (GNN) has been extensively used in histopathology whole slide image (WSI) analysis due to the efficiency and flexibility in modelling relationships among entities. However, most existing GNN-based WSI analysis methods only consider the pairwise correlation of patches from one single perspective (e.g. spatial affinity or embedding similarity) yet ignore the intrinsic non-pairwise relationships present in gigapixel WSI, which are likely to contribute to feature learning and downstream tasks. The objective of this study is therefore to explore the non-pairwise relationships in histopathology WSI and exploit them to guide the learning of slide-level representations for better classification performance.

Authors

  • Jun Shi
    School of Communication and Information Engineering, Shanghai University, Shanghai, China. Electronic address: junshi@staff.shu.edu.cn.
  • Tong Shu
    School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, 230601, Anhui Province, China.
  • Kun Wu
    Image Processing Center, School of Astronautics, Beihang University, Beijing, 102206, China.
  • Zhiguo Jiang
  • Liping Zheng
    School of Software, Hefei University of Technology, Hefei, 230601, Anhui Province, China.
  • Wei Wang
    State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macau 999078, China.
  • Haibo Wu
    Department of Pathology, the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230036, Anhui Province, China; Intelligent Pathology Institute, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230036, Anhui Province, China.
  • Yushan Zheng
    Image Processing Center, School of Astronautics, Beihang University, Beijing 100191, China; Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing 100191, China.

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