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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Improving explainable AI with patch perturbation-based evaluation pipeline: a COVID-19 X-ray image analysis case study
Jimin Sun, Wenqi Shi, Felipe Giuste, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 11

Showing 11 citing articles:

Unveiling the black box: A systematic review of Explainable Artificial Intelligence in medical image analysis
Dost Muhammad, Malika Bendechache
Computational and Structural Biotechnology Journal (2024) Vol. 24, pp. 542-560
Open Access | Times Cited: 8
Improving explanations for medical X-ray diagnosis combining variational autoencoders and adversarial machine learning
Guillermo Iglesias, Héctor D. Menéndez, Edgar Talavera
Computers in Biology and Medicine (2025) Vol. 188, pp. 109857-109857
Open Access
Transparent Insights into AI: Analyzing CNN Architecture through LIME-Based Interpretability for Land Cover Classification
Pushpalata Pujari, Himanshu Sahu
Research Square (Research Square) (2025)
Closed Access
Towards Improved XAI-Based Epidemiological Research into the Next Potential Pandemic
Hamed Khalili, Maria A. Wimmer
Life (2024) Vol. 14, Iss. 7, pp. 783-783
Open Access | Times Cited: 2
Visual Explanations and Perturbation-Based Fidelity Metrics for Feature-Based Models
Maciej Mozolewski, Szymon Bobek, Grzegorz J. Nalepa
Lecture notes in computer science (2024), pp. 294-309
Closed Access
RADAR-MIX: How to Uncover Adversarial Attacks in Medical Image Analysis through Explainability
Erikson J. de Aguiar, Caetano Traina, Agma J. M. Traina
(2024), pp. 436-441
Closed Access
Intersection of Performance, Interpretability, and Fairness in Neural Prototype Tree for Chest X-ray Pathology Detection: Algorithm Development and Validation Study (Preprint)
Hongbo Chen, Myrtede Alfred, Andrew D. Brown, et al.
(2024)
Closed Access
Intersection of Performance, Interpretability, and Fairness in Neural Prototype Tree for Chest X-Ray Pathology Detection: Algorithm Development and Validation Study
Hongbo Chen, Myrtede Alfred, Andrew D. Brown, et al.
JMIR Formative Research (2024) Vol. 8, pp. e59045-e59045
Open Access
Current status and future directions of explainable artificial intelligence in medical imaging
Shier Nee Saw, Yet Yen Yan, Kwan Hoong Ng
European Journal of Radiology (2024) Vol. 183, pp. 111884-111884
Closed Access
Effective Surrogate Models for Docking Scores Prediction of Candidate Drug Molecules on SARS-CoV-2 Protein Targets
Wenqi Shi, Mio Murakoso, Xiaoyan Guo, et al.
2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (2023) Vol. 8, pp. 4235-4242
Closed Access | Times Cited: 1
Development of Interpretable Machine Learning Models for COVID-19 Drug Target Docking Scores Prediction
Wenqi Shi, Mio Murakoso, Xiaoyan Guo, et al.
2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (2023) Vol. abs/2308.01921, pp. 4124-4131
Closed Access
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