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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 modeling using normal mammograms for predicting breast cancer risk
Dooman Arefan, Aly A. Mohamed, Wendie A. Berg, et al.
Medical Physics (2019) Vol. 47, Iss. 1, pp. 110-118
Open Access | Times Cited: 103

Showing 1-25 of 103 citing articles:

A scoping review of transfer learning research on medical image analysis using ImageNet
Mohammad Amin Morid, Alireza Borjali, Guilherme Del Fiol
Computers in Biology and Medicine (2020) Vol. 128, pp. 104115-104115
Open Access | Times Cited: 368
A survey on deep learning in medicine: Why, how and when?
Francesco Piccialli, Vittorio Di Somma, Fabio Giampaolo, et al.
Information Fusion (2020) Vol. 66, pp. 111-137
Closed Access | Times Cited: 290
From patterns to patients: Advances in clinical machine learning for cancer diagnosis, prognosis, and treatment
Kyle Swanson, Eric Q. Wu, Angela Zhang, et al.
Cell (2023) Vol. 186, Iss. 8, pp. 1772-1791
Closed Access | Times Cited: 256
Applying deep learning in digital breast tomosynthesis for automatic breast cancer detection: A review
Jun Bai, Russell Posner, Tianyu Wang, et al.
Medical Image Analysis (2021) Vol. 71, pp. 102049-102049
Open Access | Times Cited: 123
Survey on Machine Learning and Deep Learning Applications in Breast Cancer Diagnosis
Gunjan Chugh, Shailender Kumar, Nanhay Singh
Cognitive Computation (2021) Vol. 13, Iss. 6, pp. 1451-1470
Closed Access | Times Cited: 116
Prediction of Breast Cancer using Machine Learning Approaches
Reza Rabiei
Journal of Biomedical Physics and Engineering (2022) Vol. 12, Iss. 3
Open Access | Times Cited: 87
Automated detection and forecasting of COVID-19 using deep learning techniques: A review
Afshin Shoeibi, Marjane Khodatars, Mahboobeh Jafari, et al.
Neurocomputing (2024) Vol. 577, pp. 127317-127317
Open Access | Times Cited: 55
Beyond Breast Density: Risk Measures for Breast Cancer in Multiple Imaging Modalities
Raymond J. Acciavatti, Su Hyun Lee, Beatriu Reig, et al.
Radiology (2023) Vol. 306, Iss. 3
Open Access | Times Cited: 49
Improved early detection accuracy for breast cancer using a deep learning framework in medical imaging
RICHA RICHA, B. D. K. Patro
Computers in Biology and Medicine (2025) Vol. 187, pp. 109751-109751
Closed Access | Times Cited: 2
Automated Detection and Forecasting of COVID-19 using Deep Learning Techniques: A Review
Afshin Shoeibi, Marjane Khodatars, Mahboobeh Jafari, et al.
arXiv (Cornell University) (2020)
Open Access | Times Cited: 135
The digital biomarker discovery pipeline: An open-source software platform for the development of digital biomarkers using mHealth and wearables data
Brinnae Bent, Ke Wang, Emilia Grzesiak, et al.
Journal of Clinical and Translational Science (2020) Vol. 5, Iss. 1
Open Access | Times Cited: 72
Artificial intelligence in mammographic phenotyping of breast cancer risk: a narrative review
Aimilia Gastounioti, Shyam Desai, Vinayak S. Ahluwalia, et al.
Breast Cancer Research (2022) Vol. 24, Iss. 1
Open Access | Times Cited: 68
Breast lesions classifications of mammographic images using a deep convolutional neural network-based approach
Tariq Mahmood, Jianqiang Li, Yan Pei, et al.
PLoS ONE (2022) Vol. 17, Iss. 1, pp. e0263126-e0263126
Open Access | Times Cited: 62
Radiomics and artificial intelligence in breast imaging: a survey
Tianyu Zhang, Tao Tan, Riccardo Samperna, et al.
Artificial Intelligence Review (2023) Vol. 56, Iss. S1, pp. 857-892
Closed Access | Times Cited: 23
Breast cancer risk prediction using machine learning: a systematic review
Sadam Hussain, Mansoor Ali, Usman Naseem, et al.
Frontiers in Oncology (2024) Vol. 14
Open Access | Times Cited: 12
New Frontiers in Breast Cancer Imaging: The Rise of AI
Stephanie Shamir, Arielle Sasson, Laurie R. Margolies, et al.
Bioengineering (2024) Vol. 11, Iss. 5, pp. 451-451
Open Access | Times Cited: 9
Embedded Machine Learning Using Microcontrollers in Wearable and Ambulatory Systems for Health and Care Applications: A Review
Maha S. Diab, Esther Rodriguez–Villegas
IEEE Access (2022) Vol. 10, pp. 98450-98474
Open Access | Times Cited: 35
Attention-Based Ensemble Network for Effective Breast Cancer Classification over Benchmarks
Su Myat Thwin, Sharaf J. Malebary, Anas W. Abulfaraj, et al.
Technologies (2024) Vol. 12, Iss. 2, pp. 16-16
Open Access | Times Cited: 7
Use of an AI Score Combining Cancer Signs, Masking, and Risk to Select Patients for Supplemental Breast Cancer Screening
Y Liu, Moein Sorkhei, Karin Dembrower, et al.
Radiology (2024) Vol. 311, Iss. 1
Closed Access | Times Cited: 6
Understanding Clinical Mammographic Breast Density Assessment: a Deep Learning Perspective
Aly A. Mohamed, Yahong Luo, Hong Peng, et al.
Journal of Digital Imaging (2017) Vol. 31, Iss. 4, pp. 387-392
Open Access | Times Cited: 52
New convolutional neural network model for screening and diagnosis of mammograms
Chen Zhang, Jumin Zhao, Jing Niu, et al.
PLoS ONE (2020) Vol. 15, Iss. 8, pp. e0237674-e0237674
Open Access | Times Cited: 40
A Bottom-Up Review of Image Analysis Methods for Suspicious Region Detection in Mammograms
Parita Oza, Paawan Sharma, Samir Patel, et al.
Journal of Imaging (2021) Vol. 7, Iss. 9, pp. 190-190
Open Access | Times Cited: 40
A Review of Applications of Machine Learning in Mammography and Future Challenges
Sai Batchu, Fan Liu, Ahmad Amireh, et al.
Oncology (2021) Vol. 99, Iss. 8, pp. 483-490
Open Access | Times Cited: 34
Deep learning of longitudinal mammogram examinations for breast cancer risk prediction
Saba Dadsetan, Dooman Arefan, Wendie A. Berg, et al.
Pattern Recognition (2022) Vol. 132, pp. 108919-108919
Open Access | Times Cited: 25
Artificial intelligence in breast cancer imaging: risk stratification, lesion detection and classification, treatment planning and prognosis—a narrative review
Maurizio Cè, Elena Caloro, Maria Elena Pellegrino, et al.
Exploration of Targeted Anti-tumor Therapy (2022), pp. 795-816
Open Access | Times Cited: 23
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