Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Review-Reference-Cited by-同舟云学术

Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Review

Published:2023 Issue: Volume:16 Page:5-21
ISSN:1937-3333
Container-title:IEEE Reviews in Biomedical Engineering
language:
Short-container-title:IEEE Rev. Biomed. Eng.

Author:

Giuste Felipe¹^ORCID,Shi Wenqi²^ORCID,Zhu Yuanda²^ORCID,Naren Tarun³,Isgut Monica⁴^ORCID,Sha Ying¹^ORCID,Tong Li¹^ORCID,Gupte Mitali¹,Wang May D.¹^ORCID

Affiliation:

1. Wallace H. Coulter School of Biomedical Engineering, Georgia Institute of Technology, Emory University, Atlanta, GA, USA

2. Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA

3. Department of Nuclear and Radiological Engineering, Georgia Institute of Technology, Atlanta, GA, USA

4. School of Biology, Georgia Institute of Technology, Atlanta, GA, USA

Funder

Wallace H. Coulter Distinguished Faculty Fellowship

Petit Institute Faculty Fellowship

Microsoft Research

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Biomedical Engineering

Link

http://xplorestaging.ieee.org/ielx7/4664312/10007429/09804787.pdf?arnumber=9804787

Reference119 articles.

1. An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department

2. A deep convolutional neural network for COVID-19 detection using chest X-rays

3. Machine learning automatically detects COVID-19 using chest CTs in a large multicenter cohort

4. JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation

5. Striving for simplicity: The all convolutional net;springenberg,2014

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