Machine Learning in the Nuclear Medicine: Part 2-Neural Networks and Clinical Aspects
Abstract
This article is the second part in our machine learning series. Part 1 provided a general overview of machine learning in nuclear medicine. Part 2 focuses on neural networks. We start with an example illustrating how neural networks work and a discussion of potential applications. Recognizing that there is a spectrum of applications, we focus on recent publications in the areas of image reconstruction, low-dose PET, disease detection, and models used for diagnosis and outcome prediction. Finally, since the way machine learning algo- rithms are reported in the literature is extremely variable, we conclude with a call to arms regarding the need for standardized reporting of design and outcome metrics and we propose a basic checklist our community might follow going forward.
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Cite this version of the work
Katherine Zukotynski, Vincent C. Gaudet, Carlos F. Uribe, Sulantha Mathotaarachchi, Kenneth C. Smith, Pedro Rosa-Neto, Francois Benard, Sandra E. Black
(2021).
Machine Learning in the Nuclear Medicine: Part 2-Neural Networks and Clinical Aspects. UWSpace.
http://hdl.handle.net/10012/20081
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