reviewAnnual Review of Biomedical EngineeringMar 16, 2017BRONZE OA

Deep Learning in Medical Image Analysis

University of North Carolina at Chapel Hill · Korea University

PubMed
Indexed incrossrefpubmed

Abstract

This review covers computer-assisted analysis of images in the field of medical imaging. Recent advances in machine learning, especially with regard to deep learning, are helping to identify, classify, and quantify patterns in medical images. At the core of these advances is the ability to exploit hierarchical feature representations learned solely from data, instead of features designed by hand according to domain-specific knowledge. Deep learning is rapidly becoming the state of the art, leading to enhanced performance in various medical applications. We introduce the fundamentals of deep learning methods and review their successes in image registration, detection of anatomical and cellular structures,…

Citation impact

4,739
total citations
FWCI
279.48
Percentile
100%
References
123
Citations per year

Authors

3

Topics & keywords

Keywords
  • Deep learning
  • Artificial intelligence
  • Computer science
  • Exploit
  • Medical imaging
  • Segmentation
  • Field (mathematics)
  • Feature (linguistics)
UN Sustainable Development Goals
  • Quality Education
No related works found for this paper.

Funding