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Deep-Learning-Based Molecular Imaging Biomarkers: Toward Data-Driven Theranostics
Prog. Med. Phys. 2019;30(2):39-48
Published online June 30, 2019
© 2019 Korean Society of Medical Physics.

Hongyoon Choi

Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Korea
Correspondence to: Hongyoon Choi (chy1000@gmail.com)
Tel: 82-2-2072-3347
Fax: 82-2-745-7690
Received April 19, 2019; Revised May 11, 2019; Accepted May 11, 2019.
This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Deep learning has been applied to various medical data. In particular, current deep learning models exhibit remarkable performance at specific tasks, sometimes offering higher accuracy than that of experts for discriminating specific diseases from medical images. The current status of deep learning applications to molecular imaging can be divided into a few subtypes in terms of their purposes: differential diagnostic classification, enhancement of image acquisition, and image-based quantification. As functional and pathophysiologic information is key to molecular imaging, this review will emphasize the need for accurate biomarker acquisition by deep learning in molecular imaging. Furthermore, this review addresses practical issues that include clinical validation, data distribution, labeling issues, and harmonization to achieve clinically feasible deep learning models. Eventually, deep learning will enhance the role of theranostics, which aims at precision targeting of pathophysiology by maximizing molecular imaging functional information.
Keywords : Deep learning, Molecular imaging, Theranostics, Medical imaging, Imaging biomarker


June 2019, 30 (2)