Classification of renal biopsy direct immunofluorescence image using multiple attention convolutional neural network.

Journal: Computer methods and programs in biomedicine
Published Date:

Abstract

BACKGROUND AND OBJECTIVES: Direct immunofluorescence (DIF) is an important medical evaluation tool for renal pathology. In the DIF images, the deposition appearances and locations of immunoglobulin on glomeruli involve immunological characteristics of glomerulonephritis and thus can be used to aid in the identification of glomerulonephritis disease. Manual classification to such deposition patterns is time consuming and may lead to significant inter and intra operator variances. We wanted to automate the identification and fusion of deposition location and deposition appearance to assist physicians in achieving immunofluorescence reporting.

Authors

  • Liang Zhang
  • Ming Li
    Radiology Department, Huadong Hospital, Affiliated with Fudan University, Shanghai, China.
  • Yongfei Wu
    College of Data Science, Taiyuan University of Technology, Taiyuan, 030024, China. Electronic address: wuyongfei@tyut.edu.cn.
  • Fang Hao
    College of Data Science, Taiyuan University of Technology, Taiyuan, 030024, China.
  • Chen Wang
    Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
  • Weixia Han
    Department of Pathology, Second Hospital of Shanxi Medical University, Taiyuan, Shanxi, China.
  • Dan Niu
    Department of Pathology, Second Hospital of Shanxi Medical University, Taiyuan, Shanxi, China.
  • Wen Zheng
    College of Data Science, Taiyuan University of Technology, Taiyuan, 030024, China.