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Adversarial attacks and adversarial robustness in computational pathology

Authors :
Narmin Ghaffari Laleh
Daniel Truhn
Gregory Patrick Veldhuizen
Tianyu Han
Marko van Treeck
Roman D. Buelow
Rupert Langer
Bastian Dislich
Peter Boor
Volkmar Schulz
Jakob Nikolas Kather
Source :
Nature Communications, Vol 13, Iss 1, Pp 1-10 (2022)
Publication Year :
2022
Publisher :
Nature Portfolio, 2022.

Abstract

Artificial Intelligence can support diagnostic workflows in oncology, but they are vulnerable to adversarial attacks. Here, the authors show that convolutional neural networks are highly susceptible to white- and black-box adversarial attacks in clinically relevant classification tasks.

Subjects

Subjects :
Science

Details

Language :
English
ISSN :
20411723
Volume :
13
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Nature Communications
Publication Type :
Academic Journal
Accession number :
edsdoj.f6580ad98aea4da49e7134c3816a8c59
Document Type :
article
Full Text :
https://doi.org/10.1038/s41467-022-33266-0