Interpretable classification of pathology whole-slide images using attention based context-aware graph convolutional neural network.

Journal: Computer methods and programs in biomedicine
Published Date: Nov 24, 2022

Abstract

BACKGROUND AND OBJECTIVE: Whole slide image (WSI) classification and lesion localization within giga-pixel slide are challenging tasks in computational pathology that requires context-aware representations of histological features to adequately infer nidus. The existing weakly supervised learning methods mainly treat different locations in the slide as independent regions and cannot learn potential nonlinear interactions between instances based on i.i.d assumption, resulting in the model unable to effectively utilize context-ware information to predict the labels of WSIs and locate the region of interest (ROI).

Authors

  • Meiyan Liang
    School of Physics and Electronic Engineering, Shanxi University, Taiyuan 030006, China. Electronic address: meiyanliang@sxu.edu.cn.
  • Qinghui Chen
    School of Physics and Electronic Engineering, Shanxi University, Taiyuan 030006, China.
  • Bo Li
    Electric Power Research Institute, Yunnan Power Grid Co., Ltd., Kunming, Yunnan, China.
  • Lin Wang
    Department of Engineering Mechanics, Tsinghua University, Beijing 100084, China.
  • Ying Wang
    Key Laboratory of Macromolecular Science of Shaanxi Province, School of Chemistry & Chemical Engineering, Shaanxi Normal University, Xi'an, Shaanxi 710062, China.
  • Yu Zhang
    College of Marine Electrical Engineering, Dalian Maritime University, Dalian, China.
  • Ru Wang
    Dell Technologies, Round Rock, TX, USA.
  • Xing Jiang
    Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province, Hebei University of Technology, Tianjin, China.
  • Cunlin Zhang
    Beijing Key Laboratory for Terahertz Spectroscopy and Imaging, Key Laboratory of Terahertz, Optoelectronics, Ministry of Education, Capital Normal University, Beijing 100048, China.

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