Machine Learning in the Nuclear Medicine: Part 2-Neural Networks and Clinical Aspects
| dc.contributor.author | Zukotynski, Katherine | |
| dc.contributor.author | Gaudet, Vincent C. | |
| dc.contributor.author | Uribe, Carlos F. | |
| dc.contributor.author | Mathotaarachchi, Sulantha | |
| dc.contributor.author | Smith, Kenneth C. | |
| dc.contributor.author | Rosa-Neto, Pedro | |
| dc.contributor.author | Benard, Francois | |
| dc.contributor.author | Black, Sandra E. | |
| dc.date.accessioned | 2023-11-03T17:37:27Z | |
| dc.date.available | 2023-11-03T17:37:27Z | |
| dc.date.issued | 2021-01 | |
| dc.description | COPYRIGHT © 2021 by the Society of Nuclear Medicine and Molecular Imaging. | en |
| dc.description.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. | en |
| dc.identifier.uri | https://doi.org/10.2967/jnumed.119.231837 | |
| dc.identifier.uri | http://hdl.handle.net/10012/20081 | |
| dc.language.iso | en | en |
| dc.publisher | Society of Nuclear Medicine and Molecular Imaging | en |
| dc.relation.ispartofseries | Journal of Nuclear Medicine;62(1) | |
| dc.subject | machine learning | en |
| dc.subject | nuclear medicine | en |
| dc.subject | neural networks | en |
| dc.title | Machine Learning in the Nuclear Medicine: Part 2-Neural Networks and Clinical Aspects | en |
| dc.type | Article | en |
| dcterms.bibliographicCitation | Zukotynski, K., Gaudet, V., Uribe, C. F., Mathotaarachchi, S., Smith, K. C., Rosa-Neto, P., Bénard, F., & Black, S. E. (2020). Machine learning in nuclear medicine: Part 2—neural networks and clinical aspects. Journal of Nuclear Medicine, 62(1), 22–29. https://doi.org/10.2967/jnumed.119.231837 | en |
| uws.contributor.affiliation1 | Faculty of Engineering | en |
| uws.contributor.affiliation2 | Electrical and Computer Engineering | en |
| uws.peerReviewStatus | Reviewed | en |
| uws.scholarLevel | Faculty | en |
| uws.typeOfResource | Text | en |