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.

Dinggang Shen

Guorong Wu

Heung-Il Suk

Annual Review of Biomedical Engineering

自 1999 年出版的《生物医学工程年度回顾》涵盖了生物医学工程广泛领域的重大发展，包括生物力学、生物材料、计算基因组学和蛋白质组学、组织工程、生物监测、医疗保健工程、药物输送、生物电工程、生化工程和生物医学成像主题。

The Annual Review of Biomedical Engineering, published since 1999, covers significant developments across a wide range of topics in biomedical engineering, including biomechanics, biomaterials, computational genomics and proteomics, tissue engineering, biosensing, healthcare engineering, drug delivery, bioelectrical engineering, biochemical engineering, and biomedical imaging.

《Annual Review of Biomedical Engineering》自1999年出版，涵盖了生物医学工程领域的多个重要主题，包括生物力学、生物材料、计算基因组学与蛋白质组学、组织工程、生物监测、医疗工程、药物输送、生物电工程、生化工程以及生物医学成像等。

Category	Quartile
工程技术	1
工程技术, 工程生物医学	1

Deep Learning in Medical Image Analysis

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Journal Info

Category	Quartile
ENGINEERING, BIOMEDICAL	1