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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:

Development and validation of a deep learning model for breast lesion segmentation and characterization in multiparametric MRI
Jingjin Zhu, Jiahui Geng, W. Shan, et al.
Frontiers in Oncology (2022) Vol. 12
Open Access | Times Cited: 16

Showing 16 citing articles:

The Role of Deep Learning in Advancing Breast Cancer Detection Using Different Imaging Modalities: A Systematic Review
Mohammad Madani, Mohammad Mahdi Behzadi, Sheida Nabavi
Cancers (2022) Vol. 14, Iss. 21, pp. 5334-5334
Open Access | Times Cited: 52
Clinical applications of deep learning in breast MRI
Xue Zhao, Jing‐Wen Bai, Qiu Guo, et al.
Biochimica et Biophysica Acta (BBA) - Reviews on Cancer (2023) Vol. 1878, Iss. 2, pp. 188864-188864
Closed Access | Times Cited: 25
Computer‐Aided Detection (CADe) and Segmentation Methods for Breast Cancer Using Magnetic Resonance Imaging (MRI)
Payam Jannatdoust, Parya Valizadeh, Nikoo Saeedi, et al.
Journal of Magnetic Resonance Imaging (2025)
Closed Access | Times Cited: 1
Deep learning-based breast cancer diagnosis in breast MRI: systematic review and meta-analysis
Kamarul Amin Abdullah, Sara Marziali, Muzna Nanaa, et al.
European Radiology (2025)
Open Access | Times Cited: 1
Unsupervised Learning Techniques for Breast Lesion Segmentation on MRI Images: Are We Ready for Automation?
Marina Fedon Vocaturo, Luisa Altabella, Giuseppe Cardano, et al.
Applied Sciences (2025) Vol. 15, Iss. 5, pp. 2401-2401
Open Access | Times Cited: 1
AI Applications to Breast MRI: Today and Tomorrow
Roberto Lo Gullo, Joren Brunekreef, Eric Marcus, et al.
Journal of Magnetic Resonance Imaging (2024) Vol. 60, Iss. 6, pp. 2290-2308
Closed Access | Times Cited: 5
Clinical Application of Artificial Intelligence in Breast MRI
Jong-Min Kim, Su Min Ha
Journal of the Korean Society of Radiology (2025) Vol. 86, Iss. 2, pp. 227-227
Open Access
Multiparametric MRI
Akifumi Hagiwara, Shohei Fujita, Ryo Kurokawa, et al.
Investigative Radiology (2023)
Open Access | Times Cited: 10
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: 3
The Role of Artificial Intelligence in Advancing Breast Cancer Detection Using Different Imaging Modalities: A Systematic Review
Mohammad Madani, Mohammad Mahdi Behzadi, Sheida Nabavi
(2022)
Open Access | Times Cited: 15
Automatic semantic segmentation of breast cancer in DCE-MRI using DeepLabV3+ with modified ResNet50
C. Sahaya Pushpa Sarmila Star, T. M. Inbamalar, A. Milton
Biomedical Signal Processing and Control (2024) Vol. 99, pp. 106691-106691
Closed Access | Times Cited: 2
Application of machine learning in the analysis of multiparametric MRI data for the differentiation of treatment responses in breast cancer: retrospective study
Jinhua Wang, Liang Wang, Zhongxian Yang, et al.
European Journal of Cancer Prevention (2024)
Closed Access
Cross-site Validation of AI Segmentation and Harmonization in Breast MRI
Yu Huang, Nicholas J Leotta, Lukas Hirsch, et al.
Deleted Journal (2024)
Closed Access
Deep Learning for Predicting Breast Cancer: A Systematic Review of Progress and Future Directions
Kanika Kansal, Sanjiv Sharma
Research Square (Research Square) (2023)
Open Access | Times Cited: 1
Advancements in Breast Cancer Detection: A Comprehensive Review of Deep Learning Techniques for Mammogram Analysis
Vandana Saini, Meenu Khurana, Rama Krishna Challa
(2023), pp. 1-6
Closed Access | Times Cited: 1
A background parenchymal enhancement quantification framework of breast magnetic resonance imaging
Boya Zhang, Jingjin Zhu, Peifang Zhang, et al.
Quantitative Imaging in Medicine and Surgery (2023) Vol. 13, Iss. 12, pp. 8350-8357
Open Access
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