AI for radiographic COVID-19 detection selects shortcuts over signal

Author:

DeGrave Alex J.ORCID,Janizek Joseph D.ORCID,Lee Su-InORCID

Funder

NSF | BIO | Division of Biological Infrastructure

U.S. Department of Health & Human Services | National Institutes of Health

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Networks and Communications,Computer Vision and Pattern Recognition,Human-Computer Interaction,Software

Reference53 articles.

1. Mossa-Basha, M. et al. Policies and guidelines for COVID-19 preparedness: experiences from the University of Washington. Radiology https://doi.org/10.1148/radiol.2020201326 (2020).

2. Kundu, S., Elhalawani, H., Gichoya, J. W. & Kahn, C. E.Jr How might AI and chest imaging help unravel COVID-19’s mysteries? Radiol. Artificial Intell 2, 3 (2020).

3. Ghoshal, B. & Tucker, A. Estimating uncertainty and interpretability in deep learning for coronavirus (COVID-19) detection. Preprint at https://arxiv.org/pdf/2003.10769.pdf (2020).

4. Wang, L., Lin, Z. Q. & Wong, A. COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images. Sci. Rep. 10, 19549 (2020).

5. Hemdan, E. E.-D., Shouman, M. A. & Karar, M. E. COVIDX-Net: a framework of deep learning classifiers to diagnose COVID-19 in X-ray images. Preprint at https://arxiv.org/pdf/2003.11055.pdf (2020).

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