Deep Learning for Image Analysis in Kidney Care

Hanjie Zhang*, Max Botler, Jeroen P Kooman

*Corresponding author for this work

Research output: Contribution to journal(Systematic) Review article peer-review

Abstract

Analysis of medical images, such as radiological or tissue specimens, is an indispensable part of medical diagnostics. Conventionally done manually, the process may sometimes be time-consuming and prone to interobserver variability. Image classification and segmentation by deep learning strategies, predominantly convolutional neural networks, may provide a significant advance in the diagnostic process. In renal medicine, most evidence has been generated around the radiological assessment of renal abnormalities and histological analysis of renal biopsy specimens' segmentation. In this article, the basic principles of image analysis by convolutional neural networks, brief descriptions of convolutional neural networks, and their system architecture for image analysis are discussed, in combination with examples regarding their use in image analysis in nephrology.

Original languageEnglish
Pages (from-to)25-32
Number of pages8
JournalAdvances in kidney disease and health
Volume30
Issue number1
DOIs
Publication statusPublished - Jan 2023

Keywords

  • Deep Learning
  • Neural Networks, Computer
  • Image Processing, Computer-Assisted/methods

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