Anatomical context protects deep learning from adversarial perturbations in medical imaging-Reference-Cited by-同舟云学术

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Anatomical context protects deep learning from adversarial perturbations in medical imaging

Published:2020-02 Issue: Volume:379 Page:370-378
ISSN:0925-2312
Container-title:Neurocomputing
language:en
Short-container-title:Neurocomputing

Author:

Li Yi,Zhang Huahong,Bermudez Camilo,Chen Yifan,Landman Bennett A.^ORCID,Vorobeychik Yevgeniy

Funder

National Institutes of Health

Army Research Office

National Center for Research Resources

National Science Foundation

National Center for Advancing Translational Sciences

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference28 articles.

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2. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs;Gulshan;J. Am. Med. Assoc.,2016

3. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer;Bejnordi;J. Am. Med. Assoc.,2017

4. Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker;Cole;NeuroImage,2017

5. F.D.A. approval letter: Arterys software v2.0 (Oct 2016). https://www.accessdata.fda.gov/cdrh_docs/pdf16/K162513.pdf.

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