A state-of-the-art survey of artificial neural networks for Whole-slide Image analysis: From popular Convolutional Neural Networks to potential visual transformers.

Journal: Computers in biology and medicine
Published Date:

Abstract

In recent years, with the advancement of computer-aided diagnosis (CAD) technology and whole slide image (WSI), histopathological WSI has gradually played a crucial aspect in the diagnosis and analysis of diseases. To increase the objectivity and accuracy of pathologists' work, artificial neural network (ANN) methods have been generally needed in the segmentation, classification, and detection of histopathological WSI. However, the existing review papers only focus on equipment hardware, development status and trends, and do not summarize the art neural network used for full-slide image analysis in detail. In this paper, WSI analysis methods based on ANN are reviewed. Firstly, the development status of WSI and ANN methods is introduced. Secondly, we summarize the common ANN methods. Next, we discuss publicly available WSI datasets and evaluation metrics. These ANN architectures for WSI processing are divided into classical neural networks and deep neural networks (DNNs) and then analyzed. Finally, the application prospect of the analytical method in this field is discussed. The important potential method is Visual Transformers.

Authors

  • Weiming Hu
  • Xintong Li
    Medical Robotics Laboratory, School of AutomationBeijing University of Posts and TelecommunicationsBeijing100876China.
  • Chen Li
    School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, China.
  • Rui Li
    Department of Oncology, Xiyuan Hospital, China Academy of Chinese Medical Science, Beijing, China.
  • Tao Jiang
    Department of Respiratory and Critical Care Medicine, Center for Respiratory Medicine, the Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, China.
  • Hongzan Sun
    Shengjing Hospital, China Medical University, Shenyang, 110001, China.
  • Xinyu Huang
    Institute of Medical Informatics, University of Lübeck, Ratzeburger Allee 160, Lübeck 23538, Germany. Electronic address: huang@imi.uni-luebeck.de.
  • Marcin Grzegorzek
    Institute for Vision and Graphics, University of Siegen, Hoerlindstr. 3, 57076 Siegen, Germany.
  • Xiaoyan Li
    Shulan International Medical College, Zhejiang Shuren University, Hangzhou, Zhejiang, China.