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Radiomics and deep learning in liver diseases

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Abstract
Recently, radiomics and deep learning have gained attention as methods for computerized image analysis. Radiomics and deep learning can perform diagnostic or predictive tasks using high-dimensional image-derived features and have the potential to expand the capabilities of liver imaging beyond the scope of traditional visual image analysis. Recent research has demonstrated the potential of these techniques in various fields of liver imaging, including staging of liver fibrosis, prognostication of malignant liver tumors, automated detection and characterization of liver tumors, automated abdominal organ segmentation, and body composition analysis. However, because most of the previous studies were preliminary and focused mainly on technical feasibility, further clinical validation is required for the application of radiomics and deep learning in clinical practice. In this review, we introduce the technical aspects of radiomics and deep learning and summarize the recent studies on the application of these techniques in liver radiology.
Author(s)
박범우박효정성유섭이승수
Issued Date
2021
Type
Article
Keyword
deep learninglivermachine learningradiologyradiomics
DOI
10.1111/jgh.15414
URI
https://oak.ulsan.ac.kr/handle/2021.oak/7352
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_proquest_miscellaneous_2501259585&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,Radiomics%20and%20deep%20learning%20in%20liver%20diseases&offset=0&pcAvailability=true
Publisher
JOURNAL OF GASTROENTEROLOGY AND HEPATOLOGY
Location
대한민국
Language
영어
ISSN
0815-9319
Citation Volume
36
Citation Number
3
Citation Start Page
561
Citation End Page
568
Appears in Collections:
Medicine > Medicine
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