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Radiomics and Deep Learning in Clinical Imaging: What Should We Do?

Radiomics and Deep Learning in Clinical Imaging: What Should We Do? ISSN (print) 1869-3482 ISSN (online) 1869-3474 Nuclear Medicine and Molecular Imaging (2018) 52:89–90 https://doi.org/10.1007/s13139-018-0514-0 EDITORIAL Joon Young Choi Received: 15 February 2018 /Accepted: 20 February 2018 /Published online: 10 March 2018 Korean Society of Nuclear Medicine 2018 During the past several years, radiomics and deep learning medical imaging has a potential to perform automatic le- (DL) became hot issues in medical imaging field, especial- sion detection for differential diagnoses and, also, to pro- ly in cancer imaging. Radiomics is an emerging field of vide other useful information including therapy response medical imaging that uses a series of qualitative and quan- and prognostication. In these aspects, both radiomics and titative analyses of high-throughput image features to ob- DL are closely related to each other in medical imaging tain diagnostic, predictive, or prognostic information from field. For example, the radiomics data can be easily ana- medical images. Recently, radiomics methods have been lyzed and clinically applied by the DL method, which fa- used to analyze various medical images including CT, cilitate precision medicine. Figure 1 shows the recent dra- MR, and PET to provide information regarding diagnosis, matic increased publications regarding radiomics and DL patients’ outcome, tumor phenotypes, and the gene-protein http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Nuclear Medicine and Molecular Imaging Springer Journals

Radiomics and Deep Learning in Clinical Imaging: What Should We Do?

Choi, Joon
Nuclear Medicine and Molecular Imaging , Volume 52 (2) – Mar 10, 2018
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Publisher
Springer Journals
Copyright
Copyright © 2018 by Korean Society of Nuclear Medicine
Subject
Medicine & Public Health; Nuclear Medicine; Imaging / Radiology; Orthopedics; Cardiology; Oncology
ISSN
1869-3474
eISSN
1869-3482
DOI
10.1007/s13139-018-0514-0
Publisher site
See Article on Publisher Site

Abstract

ISSN (print) 1869-3482 ISSN (online) 1869-3474 Nuclear Medicine and Molecular Imaging (2018) 52:89–90 https://doi.org/10.1007/s13139-018-0514-0 EDITORIAL Joon Young Choi Received: 15 February 2018 /Accepted: 20 February 2018 /Published online: 10 March 2018 Korean Society of Nuclear Medicine 2018 During the past several years, radiomics and deep learning medical imaging has a potential to perform automatic le- (DL) became hot issues in medical imaging field, especial- sion detection for differential diagnoses and, also, to pro- ly in cancer imaging. Radiomics is an emerging field of vide other useful information including therapy response medical imaging that uses a series of qualitative and quan- and prognostication. In these aspects, both radiomics and titative analyses of high-throughput image features to ob- DL are closely related to each other in medical imaging tain diagnostic, predictive, or prognostic information from field. For example, the radiomics data can be easily ana- medical images. Recently, radiomics methods have been lyzed and clinically applied by the DL method, which fa- used to analyze various medical images including CT, cilitate precision medicine. Figure 1 shows the recent dra- MR, and PET to provide information regarding diagnosis, matic increased publications regarding radiomics and DL patients’ outcome, tumor phenotypes, and the gene-protein

Journal

Nuclear Medicine and Molecular Imaging – Springer Journals

Published: Mar 10, 2018

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