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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:

AI-Based Cancer Detection Model for Contrast-Enhanced Mammography
Clément Jailin, Sara Mohamed, Răzvan Iordache, et al.
Bioengineering (2023) Vol. 10, Iss. 8, pp. 974-974
Open Access | Times Cited: 11

Showing 11 citing articles:

YOLO-CPC: a breast tumor detection and identification algorithm based on improved YOLOv7
Yanan Du, Wanmin Liu, Yixuan Wang, et al.
Signal Image and Video Processing (2025) Vol. 19, Iss. 3
Closed Access | Times Cited: 1
Empowering breast cancer diagnosis and radiology practice: advances in artificial intelligence for contrast-enhanced mammography
Ketki Kinkar, Brandon K.K. Fields, Mary Yamashita, et al.
Frontiers in Radiology (2024) Vol. 3
Open Access | Times Cited: 6
Empowering early detection: artificial intelligence as a tool for breast cancer diagnosis
Pratishtha Verma, Gaurav Tripathi, Roshan Singh
Elsevier eBooks (2025), pp. 121-145
Closed Access
Integration of Functional Materials in Photonic and Optoelectronic Technologies for Advanced Medical Diagnostics
Naveen Thanjavur, Laxmi Bugude, Young‐Joon Kim
Biosensors (2025) Vol. 15, Iss. 1, pp. 38-38
Open Access
ELK: Enhanced Learning Through Cross-Modal Knowledge Transfer for Lesion Detection in Limited-Sample Contrast-Enhanced Mammography Datasets
Ricardo Montoya-del-Angel, Marawan Elbatel, Jorge Patricio Castillo‐Lopez, et al.
Lecture notes in computer science (2025), pp. 221-231
Closed Access
Deep-learning model for background parenchymal enhancement classification in contrast-enhanced mammography
Elodie Ripaud, Clément Jailin, Gonzalo Iñaki Quintana, et al.
Physics in Medicine and Biology (2024) Vol. 69, Iss. 11, pp. 115013-115013
Open Access | Times Cited: 2
Artificial intelligence for breast cancer detection and its health technology assessment: A scoping review
Anisie Uwimana, Giorgio Gnecco, Massimo Riccaboni
Computers in Biology and Medicine (2024) Vol. 184, pp. 109391-109391
Open Access | Times Cited: 2
Impact of Artificial Intelligence in Nursing for Geriatric Clinical Care for Chronic Diseases: A Systematic Literature Review
Mahdieh Poodineh Moghadam, Zabih Allah Moghadam, Mohammad Reza Chalak Qazani, et al.
IEEE Access (2024) Vol. 12, pp. 122557-122587
Open Access | Times Cited: 1
Deep Learning for Contrast Enhanced Mammography - a Systematic Review
Vera Sorin, Miri Sklair‐Levy, Benjamin S. Glicksberg, et al.
medRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access
Simulated image-specific microcalcification clusters and associated mass enhancement to enhance training of a deep learning model for cancer detection in contrast-enhanced mammography
Astrid Van Camp, Henry C. Woodruff, Lesley Cockmartin, et al.
(2024), pp. 52-52
Open Access
Deep Learning for Contrast Enhanced Mammography - A Systematic Review
Vera Sorin, Miri Sklair‐Levy, Benjamin S. Glicksberg, et al.
Academic Radiology (2024)
Open Access
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