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Deep Learning Applications in Medical Image Analysis

IEEE access, 2018-01, Vol.6, p.9375-9389 [Peer Reviewed Journal]

Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2018 ;ISSN: 2169-3536 ;EISSN: 2169-3536 ;DOI: 10.1109/ACCESS.2017.2788044 ;CODEN: IAECCG

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Title:
Deep Learning Applications in Medical Image Analysis
Author: Ker, Justin ; Wang, Lipo ; Rao, Jai ; Lim, Tchoyoson
Subjects: Algorithms ; Artificial neural networks ; Convolution ; Convolutional neural networks ; Deep learning ; Diagnostic systems ; Electronic health records ; Image analysis ; Image classification ; Image segmentation ; Machine learning ; Machine learning algorithms ; Medical diagnostic imaging ; medical image analysis ; Medical imaging ; Medical research ; Object recognition
Is Part Of: IEEE access, 2018-01, Vol.6, p.9375-9389
Description: The tremendous success of machine learning algorithms at image recognition tasks in recent years intersects with a time of dramatically increased use of electronic medical records and diagnostic imaging. This review introduces the machine learning algorithms as applied to medical image analysis, focusing on convolutional neural networks, and emphasizing clinical aspects of the field. The advantage of machine learning in an era of medical big data is that significant hierarchal relationships within the data can be discovered algorithmically without laborious hand-crafting of features. We cover key research areas and applications of medical image classification, localization, detection, segmentation, and registration. We conclude by discussing research obstacles, emerging trends, and possible future directions.
Publisher: Piscataway: IEEE
Language: English
Identifier: ISSN: 2169-3536
EISSN: 2169-3536
DOI: 10.1109/ACCESS.2017.2788044
CODEN: IAECCG
Source: IEEE Open Access Journals
DOAJ Directory of Open Access Journals

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Deep Learning Applications in Medical Image Analysis

Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2018 ;ISSN: 2169-3536 ;EISSN: 2169-3536 ;DOI: 10.1109/ACCESS.2017.2788044 ;CODEN: IAECCG

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