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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:

Attention Dense-U-Net for Automatic Breast Mass Segmentation in Digital Mammogram
Shuyi Li, Min Dong, Guangming Du, et al.
IEEE Access (2019) Vol. 7, pp. 59037-59047
Open Access | Times Cited: 154

Showing 1-25 of 154 citing articles:

Deep semantic segmentation of natural and medical images: a review
Saeid Asgari Taghanaki, Kumar Abhishek, Joseph Cohen, et al.
Artificial Intelligence Review (2020) Vol. 54, Iss. 1, pp. 137-178
Closed Access | Times Cited: 680
Transparency of deep neural networks for medical image analysis: A review of interpretability methods
Zohaib Salahuddin, Henry C. Woodruff, Avishek Chatterjee, et al.
Computers in Biology and Medicine (2021) Vol. 140, pp. 105111-105111
Open Access | Times Cited: 272
Breast mass segmentation in ultrasound with selective kernel U-Net convolutional neural network
Michał Byra, Piotr Jarosik, Aleksandra Szubert, et al.
Biomedical Signal Processing and Control (2020) Vol. 61, pp. 102027-102027
Open Access | Times Cited: 220
Diagnosis of breast cancer based on modern mammography using hybrid transfer learning
Aditya Khamparia, Subrato Bharati, Prajoy Podder, et al.
Multidimensional Systems and Signal Processing (2021) Vol. 32, Iss. 2, pp. 747-765
Open Access | Times Cited: 185
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: 122
Convolutional neural networks for breast cancer detection in mammography: A survey
Leila Abdelrahman, Manal Alghamdi, Fernando Collado‐Mesa, et al.
Computers in Biology and Medicine (2021) Vol. 131, pp. 104248-104248
Closed Access | Times Cited: 115
Connected-UNets: a deep learning architecture for breast mass segmentation
Asma Baccouche, Begonya García-Zapirain, Cristián Castillo-Olea, et al.
npj Breast Cancer (2021) Vol. 7, Iss. 1
Open Access | Times Cited: 108
ATT Squeeze U-Net: A Lightweight Network for Forest Fire Detection and Recognition
Jianmei Zhang, Hongqing Zhu, Pengyu Wang, et al.
IEEE Access (2021) Vol. 9, pp. 10858-10870
Open Access | Times Cited: 103
Deep Learning for Medical Image-Based Cancer Diagnosis
Xiaoyan Jiang, Zuojin Hu, Shuihua Wang‎, et al.
Cancers (2023) Vol. 15, Iss. 14, pp. 3608-3608
Open Access | Times Cited: 101
A review on the use of deep learning for medical images segmentation
Manar Aljabri, Manal Alghamdi
Neurocomputing (2022) Vol. 506, pp. 311-335
Closed Access | Times Cited: 67
U-Net-Based Models towards Optimal MR Brain Image Segmentation
Rammah Yousef, Shakir Khan, Gaurav Gupta, et al.
Diagnostics (2023) Vol. 13, Iss. 9, pp. 1624-1624
Open Access | Times Cited: 53
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: 44
Breast cancer detection and diagnosis using hybrid deep learning architecture
R. Sathesh Raaj
Biomedical Signal Processing and Control (2023) Vol. 82, pp. 104558-104558
Closed Access | Times Cited: 40
Mammography with deep learning for breast cancer detection
Lulu Wang
Frontiers in Oncology (2024) Vol. 14
Open Access | Times Cited: 14
Explainable attention based breast tumor segmentation using a combination of UNet, ResNet, DenseNet, and EfficientNet models
Shokofeh Anari, Soroush Sadeghi, Ghazaal Sheikhi, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access | Times Cited: 2
AUNet: attention-guided dense-upsampling networks for breast mass segmentation in whole mammograms
Hui Sun, Cheng Li, Boqiang Liu, et al.
Physics in Medicine and Biology (2019) Vol. 65, Iss. 5, pp. 055005-055005
Open Access | Times Cited: 128
Interpretation and visualization techniques for deep learning models in medical imaging
Daniel T Huff, Amy J. Weisman, Robert Jeraj
Physics in Medicine and Biology (2020) Vol. 66, Iss. 4, pp. 04TR01-04TR01
Open Access | Times Cited: 111
Addressing class imbalance in deep learning for small lesion detection on medical images
Alessandro Bria, Claudio Marrocco, Francesco Tortorella
Computers in Biology and Medicine (2020) Vol. 120, pp. 103735-103735
Closed Access | Times Cited: 108
Breast Cancer Segmentation Methods: Current Status and Future Potentials
Epimack Michael, He Ma, Hong Li, et al.
BioMed Research International (2021) Vol. 2021, pp. 1-29
Open Access | Times Cited: 84
Systematic Review of Computing Approaches for Breast Cancer Detection Based Computer Aided Diagnosis Using Mammogram Images
Dilovan Asaad Zebari, Dheyaa Ahmed Ibrahim, Diyar Qader Zeebaree, et al.
Applied Artificial Intelligence (2021) Vol. 35, Iss. 15, pp. 2157-2203
Open Access | Times Cited: 80
Automated Breast Mass Classification System Using Deep Learning and Ensemble Learning in Digital Mammogram
Sharaf J. Malebary, Arshad Hashmi
IEEE Access (2021) Vol. 9, pp. 55312-55328
Open Access | Times Cited: 74
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: 61
Deep convolutional neural networks for computer-aided breast cancer diagnostic: a survey
Parita Oza, Paawan Sharma, Samir Patel, et al.
Neural Computing and Applications (2022) Vol. 34, Iss. 3, pp. 1815-1836
Closed Access | Times Cited: 60
A comprehensive framework towards segmenting and classifying breast cancer patients using deep learning and Aquila optimizer
Hossam Magdy Balaha, Esraa Raffik Antar, Mahmoud M. Saafan, et al.
Journal of Ambient Intelligence and Humanized Computing (2023) Vol. 14, Iss. 6, pp. 7897-7917
Open Access | Times Cited: 33
Predicting breast cancer types on and beyond molecular level in a multi-modal fashion
Tianyu Zhang, Tao Tan, Luyi Han, et al.
npj Breast Cancer (2023) Vol. 9, Iss. 1
Open Access | Times Cited: 32
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