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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록· 키워드
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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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참고문헌 (67)
참고문헌 신청
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Russakovsky O / 2015 / Imagenet large scale visual recognition challenge / International Journal of Computer Vision 115(3):211~252
Krizhevsky A / 2012 / Imagenet classification with deep convolutional neural networks / Paper presented at: Advances in neural information processing systems2012
Weber GM / 2014 / Finding the missing link for big biomedical data / Jama 311(24):2479~2480
Esteva A / 2017 / Dermatologist-level classification of skin cancer with deep neural networks / Nature 542(7639):115~118
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