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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:

Deep learning-based automatic segmentation for size and volumetric measurement of breast cancer on magnetic resonance imaging
Wenyi Yue, Hongtao Zhang, Juan Zhou, et al.
Frontiers in Oncology (2022) Vol. 12
Open Access | Times Cited: 34

Showing 1-25 of 34 citing articles:

Recent advancements in artificial intelligence for breast cancer: Image augmentation, segmentation, diagnosis, and prognosis approaches
Jiadong Zhang, Jiaojiao Wu, Xiang Sean Zhou, et al.
Seminars in Cancer Biology (2023) Vol. 96, pp. 11-25
Closed Access | Times Cited: 51
AI in Breast Cancer Imaging: An Update and Future Trends
Yizhou Chen, Xiaoliang Shao, Kuangyu Shi, et al.
Seminars in Nuclear Medicine (2025)
Open Access | Times Cited: 3
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: 56
Deep Learning and Reinforcement Learning

Artificial intelligence (2023)
Open Access | Times Cited: 30
Automated segmentation of brain metastases in T1-weighted contrast-enhanced MR images pre and post stereotactic radiosurgery
Hemalatha Kanakarajan, Wouter De Baene, Patrick E. J. Hanssens, et al.
BMC Medical Imaging (2025) Vol. 25, Iss. 1
Open Access | Times Cited: 1
DRD-UNet, a UNet-Like Architecture for Multi-Class Breast Cancer Semantic Segmentation
Mauricio Alberto Ortega-Ruíz, Cefa Karabağ, Edgar Román-Rangel, et al.
IEEE Access (2024) Vol. 12, pp. 40412-40424
Open Access | Times Cited: 6
3D Breast Cancer Segmentation in DCE‐MRI Using Deep Learning With Weak Annotation
Ga Eun Park, Sung Hun Kim, Yoonho Nam, et al.
Journal of Magnetic Resonance Imaging (2023) Vol. 59, Iss. 6, pp. 2252-2262
Closed Access | Times Cited: 11
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: 4
Predicting Breast Cancer Subtypes Using Magnetic Resonance Imaging Based Radiomics With Automatic Segmentation
Wenyi Yue, Hongtao Zhang, Shen Gao, et al.
Journal of Computer Assisted Tomography (2023)
Open Access | Times Cited: 10
Comparative analysis of deep learning architectures for breast region segmentation with a novel breast boundary proposal
Sam Narimani, Solveig Roth Hoff, Kathinka D. Kurz, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
Tumor Volume as a Predictor of Recurrence-Free Survival, but Not Overall Survival, in Early Breast Cancer
Hyoung Won Koh, Ji-Jung Jung, Hong Kyu Kim, et al.
European Journal of Surgical Oncology (2025), pp. 110024-110024
Closed Access
Automatic Segmentation and Molecular Subtype Classification of Breast Cancer Using an MRI-based Deep Learning Framework
Xiaoxia Wang, Xiaofei Hu, Churan Wang, et al.
Radiology Imaging Cancer (2025) Vol. 7, Iss. 3
Closed Access
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
Deep learning-based tumor segmentation and classification in breast MRI with 3TP method
Edson Damasceno Carvalho, Otílio Paulo da Silva Neto, Antônio Oseas de Carvalho Filho
Biomedical Signal Processing and Control (2024) Vol. 93, pp. 106199-106199
Closed Access | Times Cited: 3
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: 3
A Convolutional Neural Network-Based Auto-Segmentation Pipeline for Breast Cancer Imaging
Lucas Jian Hoong Leow, Abu Bakr Azam, Hong Qi Tan, et al.
Mathematics (2024) Vol. 12, Iss. 4, pp. 616-616
Open Access | Times Cited: 2
Automated segmentation of meningioma from contrast-enhanced T1-weighted MRI images in a case series using a marker-controlled watershed segmentation and fuzzy C-means clustering machine learning algorithm
Sana Mohammadi, Sadegh Ghaderi, Kayvan Ghaderi, et al.
International Journal of Surgery Case Reports (2023) Vol. 111, pp. 108818-108818
Open Access | Times Cited: 4
Personalized Deep Learning Model for Clinical Target Volume on Daily Cone-beam Computed Tomography in Breast Cancer Patients
Joonil Hwang, Jaehee Chun, Seungryong Cho, et al.
Advances in Radiation Oncology (2024) Vol. 9, Iss. 10, pp. 101580-101580
Open Access | Times Cited: 1
Deep Learning for Fully Automatic Tumor Segmentation on Serially Acquired Dynamic Contrast-Enhanced MRI Images of Triple-Negative Breast Cancer
Zhan Xu, David E Rauch, Rania M. Mohamed, et al.
Cancers (2023) Vol. 15, Iss. 19, pp. 4829-4829
Open Access | Times Cited: 2
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
Current Applications of AI in Medical Imaging
Gianfranco Di Salle, Salvatore Claudio Fanni, Gayanè Aghakhanyan, et al.
Imaging informatics for healthcare professionals (2023), pp. 151-165
Closed Access | Times Cited: 1
Breast MRI Segmentation by Deep Learning: Key Gaps and Challenges
Khadijeh Askaripour, Arkadiusz Żak
IEEE Access (2023) Vol. 11, pp. 117935-117946
Open Access | Times Cited: 1
A Breast Cancer Prognosis Model using PyRadiomics and Image Segmentation from MRI data
M. Sweetline Sonia, Indrajit Kalita, Debashree Devi
(2024), pp. 27-34
Open Access
Breast tumor segmentation via deep correlation analysis of multi-sequence MRI
Hongyu Wang, Tonghui Wang, Yanfang Hao, et al.
Medical & Biological Engineering & Computing (2024) Vol. 62, Iss. 12, pp. 3801-3814
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
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