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Identifying the optimal deep learning architecture and parameters for automatic beam aperture definition in 3D radiotherapy.

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Title: Identifying the optimal deep learning architecture and parameters for automatic beam aperture definition in 3D radiotherapy.
Authors: Gay, Skylar S.1, sgay1@mdanderson.org, Kisling, Kelly D.2, Anderson, Brian M.2, Zhang, Lifei1, Rhee, Dong Joo1, Nguyen, Callistus1, Netherton, Tucker1, Yang, Jinzhong1, Brock, Kristy1,3, Jhingran, Anuja4, Simonds, Hannah5, Klopp, Ann4, Beadle, Beth M.6, Court, Laurence E.1, Cardenas, Carlos E.7
Source: Journal of Applied Clinical Medical Physics; Dec2023, Vol. 24 Issue 12, p1-11, 11p
Database: Applied Science & Technology Source
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Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 173969881
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=173969881
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        Value: 10.1002/acm2.14131
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      – Code: eng
        Text: English
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        PageCount: 11
        StartPage: 1
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      – TitleFull: Identifying the optimal deep learning architecture and parameters for automatic beam aperture definition in 3D radiotherapy.
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              Text: Dec2023
              Type: published
              Y: 2023
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