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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:

Diagnosis of Benign and Malignant Breast Lesions on DCE‐MRI by Using Radiomics and Deep Learning With Consideration of Peritumor Tissue
Jiejie Zhou, Yang Zhang, Kai‐Ting Chang, et al.
Journal of Magnetic Resonance Imaging (2019) Vol. 51, Iss. 3, pp. 798-809
Open Access | Times Cited: 169

Showing 1-25 of 169 citing articles:

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: 287
Application of Deep Learning in Breast Cancer Imaging
Luuk Balkenende, Jonas Teuwen, Ritse M. Mann
Seminars in Nuclear Medicine (2022) Vol. 52, Iss. 5, pp. 584-596
Open Access | Times Cited: 96
Deep Learning-based Automatic Diagnosis of Breast Cancer on MRI Using Mask R-CNN for Detection Followed by ResNet50 for Classification
Yang Zhang, Y. Liu, Ke Nie, et al.
Academic Radiology (2023) Vol. 30, pp. S161-S171
Open Access | Times Cited: 41
Breast cancer detection using deep learning techniques: challenges and future directions
Muhammad Shahid, Azhar Imran
Multimedia Tools and Applications (2025)
Closed Access | Times Cited: 3
Novel Approaches to Screening for Breast Cancer
Ritse M. Mann, Regina J. Hooley, R. Graham Barr, et al.
Radiology (2020) Vol. 297, Iss. 2, pp. 266-285
Closed Access | Times Cited: 132
Prediction of breast cancer molecular subtypes on DCE-MRI using convolutional neural network with transfer learning between two centers
Yang Zhang, Jeon‐Hor Chen, Yezhi Lin, et al.
European Radiology (2020) Vol. 31, Iss. 4, pp. 2559-2567
Open Access | Times Cited: 106
Clinical-Radiomic Analysis for Pretreatment Prediction of Objective Response to First Transarterial Chemoembolization in Hepatocellular Carcinoma
Mingyu Chen, Jiasheng Cao, Jiahao Hu, et al.
Liver Cancer (2021) Vol. 10, Iss. 1, pp. 38-51
Open Access | Times Cited: 87
Non-Invasive Assessment of Breast Cancer Molecular Subtypes with Multiparametric Magnetic Resonance Imaging Radiomics
Doris Leithner, Marius E. Mayerhoefer, Danny F. Martinez, et al.
Journal of Clinical Medicine (2020) Vol. 9, Iss. 6, pp. 1853-1853
Open Access | Times Cited: 78
Radiomics in breast MRI: current progress toward clinical application in the era of artificial intelligence
Hiroko Satake, Satoko Ishigaki, Rintaro Ito, et al.
La radiologia medica (2021) Vol. 127, Iss. 1, pp. 39-56
Closed Access | Times Cited: 67
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: 50
How Radiomics Can Improve Breast Cancer Diagnosis and Treatment
Filippo Pesapane, P. De Marco, Anna Rapino, et al.
Journal of Clinical Medicine (2023) Vol. 12, Iss. 4, pp. 1372-1372
Open Access | Times Cited: 37
Deep learning applications to breast cancer detection by magnetic resonance imaging: a literature review
Richard Adam, Kevin Dell’Aquila, Laura Hodges, et al.
Breast Cancer Research (2023) Vol. 25, Iss. 1
Open Access | Times Cited: 35
BreastDM: A DCE-MRI dataset for breast tumor image segmentation and classification
Xiaoming Zhao, Yuehui Liao, Jiahao Xie, et al.
Computers in Biology and Medicine (2023) Vol. 164, pp. 107255-107255
Closed Access | Times Cited: 26
Intra‐ and Peritumoral Based Radiomics for Assessment of Lymphovascular Invasion in Invasive Breast Cancer
Wenyan Jiang, Ruiqing Meng, Yuan Cheng, et al.
Journal of Magnetic Resonance Imaging (2023) Vol. 59, Iss. 2, pp. 613-625
Closed Access | Times Cited: 25
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: 24
Shallow and deep learning classifiers in medical image analysis
Francesco Prinzi, Tiziana Currieri, Salvatore Gaglio, et al.
European Radiology Experimental (2024) Vol. 8, Iss. 1
Open Access | Times Cited: 10
Breast cancer classification through multivariate radiomic time series analysis in DCE-MRI sequences
Francesco Prinzi, Alessia Angela Maria Orlando, Salvatore Gaglio, et al.
Expert Systems with Applications (2024) Vol. 249, pp. 123557-123557
Open Access | Times Cited: 8
Automatic Detection and Segmentation of Breast Cancer on MRI Using Mask R-CNN Trained on Non–Fat-Sat Images and Tested on Fat-Sat Images
Yang Zhang, Si‐Wa Chan, Vivian Youngjean Park, et al.
Academic Radiology (2020) Vol. 29, pp. S135-S144
Open Access | Times Cited: 55
Recent Radiomics Advancements in Breast Cancer: Lessons and Pitfalls for the Next Future
Filippo Pesapane, Anna Rotili, Giorgio Maria Agazzi, et al.
Current Oncology (2021) Vol. 28, Iss. 4, pp. 2351-2372
Open Access | Times Cited: 54
3D DCE-MRI Radiomic Analysis for Malignant Lesion Prediction in Breast Cancer Patients
Carmelo Militello, Leonardo Rundo, Mariangela Dimarco, et al.
Academic Radiology (2021) Vol. 29, Iss. 6, pp. 830-840
Open Access | Times Cited: 47
Intra- and peritumoral radiomics on assessment of breast cancer molecular subtypes based on mammography and MRI
Shuxian Niu, Wenyan Jiang, Nannan Zhao, et al.
Journal of Cancer Research and Clinical Oncology (2021) Vol. 148, Iss. 1, pp. 97-106
Closed Access | Times Cited: 43
Deep learning in breast radiology: current progress and future directions
William C. Ou, Dogan S. Polat, Başak E. Doğan
European Radiology (2021) Vol. 31, Iss. 7, pp. 4872-4885
Closed Access | Times Cited: 42
AI-enhanced simultaneous multiparametric 18F-FDG PET/MRI for accurate breast cancer diagnosis
Valeria Romeo, Paola Clauser, Sazan Rasul, et al.
European Journal of Nuclear Medicine and Molecular Imaging (2021) Vol. 49, Iss. 2, pp. 596-608
Open Access | Times Cited: 42
Deep learning and machine intelligence: New computational modeling techniques for discovery of the combination rules and pharmacodynamic characteristics of Traditional Chinese Medicine
Dongna Li, Jing Hu, Lin Zhang, et al.
European Journal of Pharmacology (2022) Vol. 933, pp. 175260-175260
Closed Access | Times Cited: 32
Deep Learning Radiomics of Preoperative Breast MRI for Prediction of Axillary Lymph Node Metastasis in Breast Cancer
Yanhong Chen, Lijun Wang, Xue Dong, et al.
Journal of Digital Imaging (2023) Vol. 36, Iss. 4, pp. 1323-1331
Open Access | Times Cited: 20
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