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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:

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: 86

Showing 1-25 of 86 citing articles:

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: 106
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: 2
An Optimized Framework for Breast Cancer Classification Using Machine Learning
Epimack Michael, He Ma, Hong Li, et al.
BioMed Research International (2022) Vol. 2022, pp. 1-18
Open Access | Times Cited: 61
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: 28
Improving Breast Cancer Detection and Diagnosis through Semantic Segmentation Using the Unet3+ Deep Learning Framework
Taukir Alam, Wei-Chung Shia, Fang-Rong Hsu, et al.
Biomedicines (2023) Vol. 11, Iss. 6, pp. 1536-1536
Open Access | Times Cited: 23
A Progressive UNDML Framework Model for Breast Cancer Diagnosis and Classification
G. Meenalochini, D. Amutha Guka, S. Ramkumar, et al.
Data & Metadata (2024) Vol. 3, pp. 198-198
Open Access | Times Cited: 16
Enhanced breast cancer detection and classification via CAMR-Gabor filters and LSTM: A deep Learning-Based method
Vinit Kumar, K. Chandrashekhara, Naga Padmaja Jagini, et al.
Egyptian Informatics Journal (2025) Vol. 29, pp. 100602-100602
Closed Access | Times Cited: 1
MUNet: a novel framework for accurate brain tumor segmentation combining UNet and mamba networks
Lijuan Yang, Qiang Dong, Da Lin, et al.
Frontiers in Computational Neuroscience (2025) Vol. 19
Open Access | Times Cited: 1
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
Fuzzy C-Means Clustering: A Review of Applications in Breast Cancer Detection
Daniel Krasnov, D.T. Davis, Keiran Malott, et al.
Entropy (2023) Vol. 25, Iss. 7, pp. 1021-1021
Open Access | Times Cited: 20
Ensemble Learning-Based Hybrid Segmentation of Mammographic Images for Breast Cancer Risk Prediction Using Fuzzy C-Means and CNN Model
Sudan Jha, Sultan Ahmad, Anoopa Arya, et al.
Journal of Healthcare Engineering (2023) Vol. 2023, pp. 1-18
Open Access | Times Cited: 18
Segmenting Medical Images: From UNet to Res-UNet and nnUNet
Lina Huang, Alina Miron, Kate Hone, et al.
(2024), pp. 483-489
Open Access | Times Cited: 7
Automated Tumor Segmentation in Radiotherapy
Ricky R. Savjani, Michael Lauria, Supratik Bose, et al.
Seminars in Radiation Oncology (2022) Vol. 32, Iss. 4, pp. 319-329
Open Access | Times Cited: 27
Rethinking Breast Cancer Diagnosis through Deep Learning Based Image Recognition
Daewon Kwak, Jiwoo Choi, Sung‐Jin Lee
Sensors (2023) Vol. 23, Iss. 4, pp. 2307-2307
Open Access | Times Cited: 15
The Evolution of Artificial Intelligence in Medical Imaging: From Computer Science to Machine and Deep Learning
Michele Avanzo, Joseph Stancanello, G. Pirrone, et al.
Cancers (2024) Vol. 16, Iss. 21, pp. 3702-3702
Open Access | Times Cited: 6
Visual ensemble selection of deep convolutional neural networks for 3D segmentation of breast tumors on dynamic contrast enhanced MRI
Masoomeh Rahimpour, Marie-Judith Saint Martin, Frédérique Frouin, et al.
European Radiology (2022) Vol. 33, Iss. 2, pp. 959-969
Open Access | Times Cited: 21
A solution to the stagnation of multi-verse optimization: An efficient method for breast cancer pathologic images segmentation
Han Yan, Weibin Chen, Ali Asghar Heidari, et al.
Biomedical Signal Processing and Control (2023) Vol. 86, pp. 105208-105208
Closed Access | Times Cited: 12
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
Breast Delineation in Full-Field Digital Mammography Using the Segment Anything Model
Andrés Larroza, Francisco Javier Pérez‐Benito, Raquel Tendero, et al.
Diagnostics (2024) Vol. 14, Iss. 10, pp. 1015-1015
Open Access | Times Cited: 4
A windowing-based multi-view u-net for tumor segmentation in cone-beam breast CT
D Peng, Tong Su, Yineng Zheng, et al.
Biomedical Signal Processing and Control (2025) Vol. 103, pp. 107482-107482
Closed Access
Segmentation of Breast Cancer Mass in Mammogram Images Using Deep Neural Network
S. Zulaikha Beevi, S. Sahebzathi
Algorithms for intelligent systems (2025), pp. 173-187
Closed Access
Hybrid Deep Learning and Active Contour Approach for Enhanced Breast Lesion Segmentation and Classification in Mammograms
Abdala Nour, Boubakeur Boufama
Intelligence-Based Medicine (2025) Vol. 11, pp. 100224-100224
Open Access
Graph-Based Multi-Modality Network for Axillary Lymph Node Metastasis Prediction in Early-Stage Breast Cancer
Yeru Xia, Ning Qu, Yongzhong Lin, et al.
(2025)
Closed Access
A Two-Stage Lightweight Deep Learning Framework for Mass Detection and Segmentation in Mammograms Using YOLOv5 and Depthwise SegNet
D.E. Manolakis, Paschalis Bizopoulos, Antonios Lalas, et al.
Deleted Journal (2025)
Open Access
A Hybrid Model for the Segmentation of Mammogram Images using Otsu Thresholding, Morphology and U-Net
Vandana Saini, Meenu Khurana, Rama Krishna Challa
Biomedical & Pharmacology Journal (2025) Vol. 18, Iss. 1, pp. 799-812
Open Access
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