Protecting Prostate Cancer Classification From Rectal Artifacts via Targeted Adversarial Training

Author:

Hu Lei1ORCID,Zhou Dawei2ORCID,Xu Jiahua2ORCID,Lu Cheng1ORCID,Han Chu1ORCID,Shi Zhenwei1ORCID,Zhu Qikui3ORCID,Gao Xinbo4ORCID,Wang Nannan2ORCID,Liu Zaiyi1ORCID

Affiliation:

1. Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China

2. State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an, China

3. Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA

4. Chongqing Key Laboratory of Image Cognition, Chongqing University of Posts and Telecommunications, Chongqing, China

Funder

Key-Area Research and Development Program of Guangdong Province

Regional Innovation and Development Joint Fund of National Natural Science Foundation of China

National Science Foundation for Young Scientists of China

Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application

China Postdoctoral Science Foundation

National Key R&D Program of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Reference72 articles.

1. Susceptibility artifacts and PIRADS 3 lesions in prostatic MRI: how often is the dynamic contrast-enhance sequence necessary?

2. Very deep convolutional networks for large-scale image recognition;Simonyan,2015

3. Deep Residual Learning for Image Recognition

4. Wide Residual Networks

5. Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries;Bray;Glob. Cancer Statist.,2018

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