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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:

New Frontiers: An Update on Computer-Aided Diagnosis for Breast Imaging in the Age of Artificial Intelligence
Yiming Gao, Krzysztof J. Geras, Alana A. Lewin, et al.
American Journal of Roentgenology (2019) Vol. 212, Iss. 2, pp. 300-307
Open Access | Times Cited: 108

Showing 1-25 of 108 citing articles:

Deep Neural Networks Improve Radiologists’ Performance in Breast Cancer Screening
Nan Wu, Jason Phang, Jungkyu Park, et al.
IEEE Transactions on Medical Imaging (2019) Vol. 39, Iss. 4, pp. 1184-1194
Open Access | Times Cited: 552
Artificial Intelligence for Mammography and Digital Breast Tomosynthesis: Current Concepts and Future Perspectives
Krzysztof J. Geras, Ritse M. Mann, Linda Moy
Radiology (2019) Vol. 293, Iss. 2, pp. 246-259
Open Access | Times Cited: 242
AI-based computer-aided diagnosis (AI-CAD): the latest review to read first
Hiroshi Fujita
Radiological Physics and Technology (2020) Vol. 13, Iss. 1, pp. 6-19
Closed Access | Times Cited: 241
Breast Cancer Classification from Ultrasound Images Using Probability-Based Optimal Deep Learning Feature Fusion
Kiran Jabeen, Muhammad Attique Khan, Majed Alhaisoni, et al.
Sensors (2022) Vol. 22, Iss. 3, pp. 807-807
Open Access | Times Cited: 197
An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization
Yiqiu Shen, Nan Wu, Jason Phang, et al.
Medical Image Analysis (2020) Vol. 68, pp. 101908-101908
Open Access | Times Cited: 166
Artificial intelligence system reduces false-positive findings in the interpretation of breast ultrasound exams
Yiqiu Shen, Farah E. Shamout, Jamie R. Oliver, et al.
Nature Communications (2021) Vol. 12, Iss. 1
Open Access | Times Cited: 160
Connected-UNets: a deep learning architecture for breast mass segmentation
Asma Baccouche, Begonya García-Zapirain, Cristián Castillo-Olea, et al.
npj Breast Cancer (2021) Vol. 7, Iss. 1
Open Access | Times Cited: 108
Artificial Intelligence in Dermatology Image Analysis: Current Developments and Future Trends
Zhouxiao Li, Konstantin Christoph Koban, Thilo L. Schenck, et al.
Journal of Clinical Medicine (2022) Vol. 11, Iss. 22, pp. 6826-6826
Open Access | Times Cited: 98
Harnessing Artificial Intelligence to Enhance Global Breast Cancer Care: A Scoping Review of Applications, Outcomes, and Challenges
Jolene Li Ling Chia, George He, Kee Yuan Ngiam, et al.
Cancers (2025) Vol. 17, Iss. 2, pp. 197-197
Open Access | Times Cited: 1
A comparative study of pre-trained convolutional neural networks for semantic segmentation of breast tumors in ultrasound
Wilfrido Gómez‐Flores, Wagner Coelho de Albuquerque Pereira
Computers in Biology and Medicine (2020) Vol. 126, pp. 104036-104036
Closed Access | Times Cited: 76
Reviewing Machine Learning and Image Processing Based Decision-Making Systems for Breast Cancer Imaging
Hasnae Zerouaoui, Ali Idri
Journal of Medical Systems (2021) Vol. 45, Iss. 1
Closed Access | Times Cited: 73
Artificial Intelligence (AI) for Screening Mammography, From the AJR Special Series on AI Applications
Leslie R. Lamb, Constance D. Lehman, Aimilia Gastounioti, et al.
American Journal of Roentgenology (2022) Vol. 219, Iss. 3, pp. 369-380
Open Access | Times Cited: 47
Transformers Improve Breast Cancer Diagnosis from Unregistered Multi-View Mammograms
Xuxin Chen, Ke Zhang, Neman Abdoli, et al.
Diagnostics (2022) Vol. 12, Iss. 7, pp. 1549-1549
Open Access | Times Cited: 45
Emerging applications of machine learning in genomic medicine and healthcare
Narjice Chafai, L. Bonizzi, Sara Botti, et al.
Critical Reviews in Clinical Laboratory Sciences (2023) Vol. 61, Iss. 2, pp. 140-163
Closed Access | Times Cited: 30
Characterizing the Clinical Adoption of Medical AI Devices through U.S. Insurance Claims
Kevin Wu, Eric Q. Wu, Brandon Theodorou, et al.
NEJM AI (2023) Vol. 1, Iss. 1
Open Access | Times Cited: 28
A New Deep-Learning-Based Model for Breast Cancer Diagnosis from Medical Images
Salman Zakareya, Habib Izadkhah, Jaber Karimpour
Diagnostics (2023) Vol. 13, Iss. 11, pp. 1944-1944
Open Access | Times Cited: 26
Deep-Learning-Based Computer-Aided Systems for Breast Cancer Imaging: A Critical Review
Yuliana Jiménez-Gaona, María José Rodríguez-Álvarez, Vasudevan Lakshminarayanan
Applied Sciences (2020) Vol. 10, Iss. 22, pp. 8298-8298
Open Access | Times Cited: 68
Computational Radiology in Breast Cancer Screening and Diagnosis Using Artificial Intelligence
William T. Tran, Ali Sadeghi‐Naini, Fang‐I Lu, et al.
Canadian Association of Radiologists Journal (2020) Vol. 72, Iss. 1, pp. 98-108
Open Access | Times Cited: 54
MOOD 2020: A Public Benchmark for Out-of-Distribution Detection and Localization on Medical Images
David Zimmerer, Peter M. Full, Fabian Isensee, et al.
IEEE Transactions on Medical Imaging (2022) Vol. 41, Iss. 10, pp. 2728-2738
Open Access | Times Cited: 29
Breast cancer detection using machine learning approaches: a comparative study
Muawia A. Elsadig, Abdelrahman Altigani, Huwaida T. Elshoush
International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering (2022) Vol. 13, Iss. 1, pp. 736-736
Open Access | Times Cited: 29
Automated breast tumor ultrasound image segmentation with hybrid UNet and classification using fine-tuned CNN model
Shahed Hossain, Sami Azam, Sidratul Montaha, et al.
Heliyon (2023) Vol. 9, Iss. 11, pp. e21369-e21369
Open Access | Times Cited: 19
Multi-modal artificial intelligence for the combination of automated 3D breast ultrasound and mammograms in a population of women with predominantly dense breasts
Tao Tan, Alejandro Rodríguez‐Ruiz, Tianyu Zhang, et al.
Insights into Imaging (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 17
Positive Predictive Values of Abnormality Scores From a Commercial Artificial Intelligence-Based Computer-Aided Diagnosis for Mammography
Si Eun Lee, Hanpyo Hong, Eun‐Kyung Kim
Korean Journal of Radiology (2024) Vol. 25, Iss. 4, pp. 343-343
Open Access | Times Cited: 5
Segmentation in mammographic images based on the U-NET model
Shavkat Fazilov, Khabiba Abdiyeva, O.R. Yusupov, et al.
AIP conference proceedings (2024) Vol. 3147, pp. 040025-040025
Closed Access | Times Cited: 5
Hybrid transformer‐based model for mammogram classification by integrating prior and current images
Afsana Ahsan Jeny, Sahand Hamzehei, Annie Jin, et al.
Medical Physics (2025)
Closed Access
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