Deep Learning in Medical Image Analysis

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초록

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.

키워드

medical image analysis; deep learning; unsupervised feature learning; CONVOLUTIONAL NEURAL-NETWORKS; BRAIN-TUMOR SEGMENTATION; AUTOMATIC SEGMENTATION; MR-IMAGES; DEFORMABLE REGISTRATION; CT; CLASSIFICATION; REPRESENTATION; AUTOENCODERS; ARCHITECTURE
제목
Deep Learning in Medical Image Analysis
저자
Shen, Dinggang; Wu, Guorong; Suk, Heung-Il
DOI
10.1146/annurev-bioeng-071516-044442
발행일
2017
유형
Review; Book Chapter
저널명
Annual Review of Biomedical Engineering
권
19
페이지
221 ~ 248