Deep Learning in Medical Image Analysis

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 h...

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Vydáno v:Annual review of biomedical engineering Ročník 19; s. 221 - 248
Hlavní autoři: Shen, Dinggang, Wu, Guorong, Suk, Heung-Il
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States 21.06.2017
Témata:
Algorithms
Diagnostic Imaging - methods
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Neural Networks (Computer)
Pattern Recognition, Automated - methods
Unsupervised Machine Learning
unsupervised feature learning
deep learning
medical image analysis
ISSN:1545-4274
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Shrnutí: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, tissue segmentation, computer-aided disease diagnosis and prognosis, and so on. We conclude by discussing research issues and suggesting future directions for further improvement.
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ISSN:1545-4274
DOI:10.1146/annurev-bioeng-071516-044442