Self-Supervised Learning With Limited Labeled Data for Prostate Cancer Detection in High-Frequency Ultrasound-Reference-Cited by-同舟云学术

Self-Supervised Learning With Limited Labeled Data for Prostate Cancer Detection in High-Frequency Ultrasound

Published:2023-09 Issue:9 Volume:70 Page:1073-1083
ISSN:0885-3010
Container-title:IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control
language:
Short-container-title:IEEE Trans. Ultrason., Ferroelect., Freq. Contr.

Author:

Wilson Paul F. R.¹^ORCID,Gilany Mahdi¹^ORCID,Jamzad Amoon¹,Fooladgar Fahimeh²,To Minh Nguyen Nhat²,Wodlinger Brian³,Abolmaesumi Purang²^ORCID,Mousavi Parvin¹

Affiliation:

1. School of Computing, Queen’s University, Kingston, Canada

2. Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, Canada

3. Exact Imaging, Markham, Canada

Funder

Canadian Institutes for Health Research

Natural Sciences and Engineering Research Council of Canada

Canada CIFAR AI Chair

Vector Institute

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Acoustics and Ultrasonics,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/58/10234123/10190101.pdf?arnumber=10190101

Reference52 articles.

1. Augmenting Detection of Prostate Cancer in Transrectal Ultrasound Images Using SVM and RF Time Series

2. Quasi-real time multi-frequency 3D shear wave absolute vibro-elastography (S-WAVE) system for prostate;abrar aleef;arXiv 2205 04038,2022

3. Deep Recurrent Neural Networks for Prostate Cancer Detection: Analysis of Temporal Enhanced Ultrasound

4. Detection and grading of prostate cancer using temporal enhanced ultrasound: combining deep neural networks and tissue mimicking simulations

5. Stochastic gradient methods with layer-wise adaptive moments for training of deep networks;ginsburg;arXiv 1905 11286,2019

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