Injecting and removing suspicious features in breast imaging with CycleGAN: A pilot study of automated adversarial attacks using neural networks on small images-Reference-Cited by-同舟云学术

Injecting and removing suspicious features in breast imaging with CycleGAN: A pilot study of automated adversarial attacks using neural networks on small images

Published:2019-11 Issue: Volume:120 Page:108649
ISSN:0720-048X
Container-title:European Journal of Radiology
language:en
Short-container-title:European Journal of Radiology

Author:

Becker Anton S.,Jendele Lukas,Skopek Ondrej,Berger Nicole,Ghafoor Soleen,Marcon Magda,Konukoglu Ender

Publisher

Elsevier BV

Subject

Radiology, Nuclear Medicine and imaging,General Medicine

Reference31 articles.

1. Deep learning in mammography: diagnostic accuracy of a multipurpose image analysis software in the detection of breast cancer;Becker;Invest. Radiol.,2017

2. Radiomic versus convolutional neural networks analysis for classification of contrastenhancing lesions at multiparametric breast MRI;Truhn;Radiology,2018

3. Medical cyber-physical systems: a survey;Dey;J. Med. Syst.,2018

4. Deep learning;LeCun;Nat. Publ. Group,2015

5. Generative adversarial nets;Goodfellow,2014

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