PUBLICATION DATE

2012-07

TITLE

Machine learning and radiology.

ISSUE

5

VOLUME

16

ISSN (print)

N/A

ISSN (electronic)

N/A

ABSTRACT

In this paper, we give a short introduction to machine learning and survey its applications in radiology. We focused on six categories of applications in radiology: medical image segmentation, registration, computer aided detection and diagnosis, brain function or activity analysis and neurological disease diagnosis from fMR images, content-based image retrieval systems for CT or MRI images, and text analysis of radiology reports using natural language processing (NLP) and natural language understanding (NLU). This survey shows that machine learning plays a key role in many radiology applications. Machine learning identifies complex patterns automatically and helps radiologists make intelligent decisions on radiology data such as conventional radiographs, CT, MRI, and PET images and radiology reports. In many applications, the performance of machine learning-based automatic detection and diagnosis systems has shown to be comparable to that of a well-trained and experienced radiologist. Technology development in machine learning and radiology will benefit from each other in the long run. Key contributions and common characteristics of machine learning techniques in radiology are discussed. We also discuss the problem of translating machine learning applications to the radiology clinical setting, including advantages and potential barriers.

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JOURNAL BRAND

N/A (note: articles not published by Springer Nature have limited metadata)


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    14 TRIPLES      14 PREDICATES      15 URIs      10 LITERALS

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    1 articles:21a0e96b740475a7b63141b0a1b8621c sg:abstract In this paper, we give a short introduction to machine learning and survey its applications in radiology. We focused on six categories of applications in radiology: medical image segmentation, registration, computer aided detection and diagnosis, brain function or activity analysis and neurological disease diagnosis from fMR images, content-based image retrieval systems for CT or MRI images, and text analysis of radiology reports using natural language processing (NLP) and natural language understanding (NLU). This survey shows that machine learning plays a key role in many radiology applications. Machine learning identifies complex patterns automatically and helps radiologists make intelligent decisions on radiology data such as conventional radiographs, CT, MRI, and PET images and radiology reports. In many applications, the performance of machine learning-based automatic detection and diagnosis systems has shown to be comparable to that of a well-trained and experienced radiologist. Technology development in machine learning and radiology will benefit from each other in the long run. Key contributions and common characteristics of machine learning techniques in radiology are discussed. We also discuss the problem of translating machine learning applications to the radiology clinical setting, including advantages and potential barriers.
    2 sg:doi 10.1016/j.media.2012.02.005
    3 sg:doiLink http://dx.doi.org/10.1016/j.media.2012.02.005
    4 sg:isFundedPublicationOf grants:c7071ca79c5e3513f4e01545bb54b181
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    6 sg:language English
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    8 sg:publicationYear 2012
    9 sg:publicationYearMonth 2012-07
    10 sg:scigraphId 21a0e96b740475a7b63141b0a1b8621c
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    12 sg:volume 16
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