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

Research output: Contribution to journal › Article › peer-review

25 Citations (Scopus)

Original language	English
Article number	106375
Journal	Computer Methods and Programs in Biomedicine
Volume	210
DOIs	https://doi.org/10.1016/j.cmpb.2021.106375
Publication status	Published - Oct 2021

ASJC Scopus Subject Areas

Software
Health Informatics
Computer Science Applications

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1016/j.cmpb.2021.106375

Cite this

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, Article 106375. https://doi.org/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. / Hoar, David; Lee, Peter Q.; Guida, Alessandro et al.
In: Computer Methods and Programs in Biomedicine, Vol. 210, 106375, 10.2021.

Research output: Contribution to journal › Article › peer-review

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, vol. 210, 106375. https://doi.org/10.1016/j.cmpb.2021.106375

@article{461de3301dcf4461a508ca3777edfaf2,

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",

issn = "0169-2607",

publisher = "Elsevier Ireland Ltd",

}

TY - JOUR

T1 - Combined Transfer Learning and Test-Time Augmentation Improves Convolutional Neural Network-Based Semantic Segmentation of Prostate Cancer from Multi-Parametric MR Images

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.

PY - 2021/10

Y1 - 2021/10

UR - http://www.scopus.com/inward/record.url?scp=85114302101&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85114302101&partnerID=8YFLogxK

U2 - 10.1016/j.cmpb.2021.106375

DO - 10.1016/j.cmpb.2021.106375

M3 - Article

C2 - 34500139

AN - SCOPUS:85114302101

SN - 0169-2607

VL - 210

JO - Computer Methods and Programs in Biomedicine

JF - Computer Methods and Programs in Biomedicine

M1 - 106375

ER -

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

ASJC Scopus Subject Areas

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this