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KMID : 1038720190300020039
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Progress in Medical Physics
2019 Volume.30 No. 2 p.39 ~ p.48
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Deep-Learning-Based Molecular Imaging Biomarkers: Toward Data-Driven Theranostics
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Choi Hong-Yoon
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Abstract
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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.
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KEYWORD
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Deep learning, Molecular imaging, Theranostics, Medical imaging, Imaging biomarker
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