Toward Improved Interpretability in Medical Imaging: Revealing the Disease Evidence From Chest X-Ray Images Using an Adversarial Generative Approach-Reference-Cited by-同舟云学术

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Toward Improved Interpretability in Medical Imaging: Revealing the Disease Evidence From Chest X-Ray Images Using an Adversarial Generative Approach

Published:2024 Issue: Volume:12 Page:82002-82014
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Qin Xiaoli¹^ORCID,Bui Francis Minhthang¹^ORCID,Han Zhu²^ORCID,Khademi April³

Affiliation:

1. Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, SK, Canada

2. Department of Electrical and Computer Engineering, University of Houston, Houston, TX, USA

3. Department of Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON, Canada

Funder

National Science Foundation

Chinese Scholarship Council

Natural Sciences and Engineering Research Council of Canada

U.S. Department of Transportation

Toyota

Amazon

Japan Science and Technology Agency (JST) Adopting Sustainable Partnerships for Innovative Research Ecosystem

Canada Research Chairs Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

https://ieeexplore.ieee.org/ielam/6287639/10380310/10552760-aam.pdf

Reference44 articles.

1. Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning

2. SpineNet: Automatically Pinpointing Classification Evidence in Spinal MRIs

3. SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound

4. Discriminative Localization in CNNs for Weakly-Supervised Segmentation of Pulmonary Nodules

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