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      Deep Learning in Medical Image Analysis

      1 , 2 , 1 , 2

      Annual Review of Biomedical Engineering

      Annual Reviews

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          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, 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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          Most cited references 71

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          Is Open Access

          Deep Learning in Neural Networks: An Overview

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          In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarises relevant work, much of it from the previous millennium. Shallow and deep learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.
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            Object recognition from local scale-invariant features

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              DeepFace: Closing the Gap to Human-Level Performance in Face Verification

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                Author and article information

                Journal
                Annual Review of Biomedical Engineering
                Annu. Rev. Biomed. Eng.
                Annual Reviews
                1523-9829
                1545-4274
                June 21 2017
                June 21 2017
                : 19
                : 1
                : 221-248
                Affiliations
                [1 ]Department of Radiology, University of North Carolina, Chapel Hill, North Carolina 27599;
                [2 ]Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea;
                Article
                10.1146/annurev-bioeng-071516-044442
                5479722
                28301734
                © 2017

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