Combined Transfer Learning and Test-Time Augmentation Improves Convolutional Neural Network-Based Semantic Segmentation of Prostate Cancer from Multi-Parametric MR Images

David Hoar; Peter Q. Lee; Alessandro Guida; Steven Patterson; Chris V. Bowen; Jennifer Merrimen; Cheng Wang; Ricardo Rendon; Steven D. Beyea; Sharon E. Clarke

doi:10.1016/j.cmpb.2021.106375

Combined Transfer Learning and Test-Time Augmentation Improves Convolutional Neural Network-Based Semantic Segmentation of Prostate Cancer from Multi-Parametric MR Images

David Hoar, Peter Q. Lee, Alessandro Guida, Steven Patterson, Chris V. Bowen, Jennifer Merrimen, Cheng Wang, Ricardo Rendon, Steven D. Beyea, Sharon E. Clarke

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24 引用（Scopus）

源语言	英语
文章编号	106375
期刊	Computer Methods and Programs in Biomedicine
卷	210
DOI	https://doi.org/10.1016/j.cmpb.2021.106375
出版状态	已出版 - 10月 2021

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卫生信息学
计算机科学应用

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Hoar, D., Lee, P. Q., Guida, A., Patterson, S., Bowen, C. V., Merrimen, J., Wang, C., Rendon, R., Beyea, S. D., & Clarke, S. E. (2021). Combined Transfer Learning and Test-Time Augmentation Improves Convolutional Neural Network-Based Semantic Segmentation of Prostate Cancer from Multi-Parametric MR Images. Computer Methods and Programs in Biomedicine, 210, 文章 106375. https://doi.org/10.1016/j.cmpb.2021.106375

Hoar, D, Lee, PQ, Guida, A, Patterson, S, Bowen, CV, Merrimen, J, Wang, C, Rendon, R , Beyea, SD & Clarke, SE 2021, 'Combined Transfer Learning and Test-Time Augmentation Improves Convolutional Neural Network-Based Semantic Segmentation of Prostate Cancer from Multi-Parametric MR Images', Computer Methods and Programs in Biomedicine, 卷 210, 106375. https://doi.org/10.1016/j.cmpb.2021.106375

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title = "Combined Transfer Learning and Test-Time Augmentation Improves Convolutional Neural Network-Based Semantic Segmentation of Prostate Cancer from Multi-Parametric MR Images",

author = "David Hoar and Lee, {Peter Q.} and Alessandro Guida and Steven Patterson and Bowen, {Chris V.} and Jennifer Merrimen and Cheng Wang and Ricardo Rendon and Beyea, {Steven D.} and Clarke, {Sharon E.}",

note = "Funding Information: Drs. Guida, Bowen, Beyea and Clarke acknowledge research funding and support from GE Healthcare. Funding Information: This work is supported by the Atlantic Innovation Fund, an Investigator Sponsored Research Agreement with GE Healthcare, Brain Canada, the Radiology Research Foundation, Canada Summer Jobs Program and Nova Scotia Cooperative Education Incentive grant. This material is based upon work supported by Google Cloud. The authors would like to acknowledge Liette Connor for recruiting subjects, Manjari Murthy, Jessica Leudi, Nathan Murtha and Allister Mason for consenting participants and David McAllindon for creating the pseudo-wholemount histopathology sections. Publisher Copyright: {\textcopyright} 2021 Elsevier B.V.",

year = "2021",

month = oct,

doi = "10.1016/j.cmpb.2021.106375",

language = "English",

volume = "210",

journal = "Computer Methods and Programs in Biomedicine",

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AU - Hoar, David

AU - Lee, Peter Q.

AU - Guida, Alessandro

AU - Patterson, Steven

AU - Bowen, Chris V.

AU - Merrimen, Jennifer

AU - Wang, Cheng

AU - Rendon, Ricardo

AU - Beyea, Steven D.

AU - Clarke, Sharon E.

N1 - Funding Information: Drs. Guida, Bowen, Beyea and Clarke acknowledge research funding and support from GE Healthcare. Funding Information: This work is supported by the Atlantic Innovation Fund, an Investigator Sponsored Research Agreement with GE Healthcare, Brain Canada, the Radiology Research Foundation, Canada Summer Jobs Program and Nova Scotia Cooperative Education Incentive grant. This material is based upon work supported by Google Cloud. The authors would like to acknowledge Liette Connor for recruiting subjects, Manjari Murthy, Jessica Leudi, Nathan Murtha and Allister Mason for consenting participants and David McAllindon for creating the pseudo-wholemount histopathology sections. Publisher Copyright: © 2021 Elsevier B.V.

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ER -

Combined Transfer Learning and Test-Time Augmentation Improves Convolutional Neural Network-Based Semantic Segmentation of Prostate Cancer from Multi-Parametric MR Images

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