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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:

Axillary lymph node metastasis status prediction of early-stage breast cancer using convolutional neural networks
Yan‐Wei Lee, Chiun‐Sheng Huang, Chung-Chih Shih, et al.
Computers in Biology and Medicine (2020) Vol. 130, pp. 104206-104206
Closed Access | Times Cited: 57

Showing 1-25 of 57 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: 45
Advancements in Deep Learning for B-Mode Ultrasound Segmentation: A Comprehensive Review
Mohammed Yusuf Ansari, Iffa Afsa Changaai Mangalote, Pramod Kumar Meher, et al.
IEEE Transactions on Emerging Topics in Computational Intelligence (2024) Vol. 8, Iss. 3, pp. 2126-2149
Closed Access | Times Cited: 40
Deep Learning and Machine Learning with Grid Search to Predict Later Occurrence of Breast Cancer Metastasis Using Clinical Data
Xia Jiang, Chuhan Xu
Journal of Clinical Medicine (2022) Vol. 11, Iss. 19, pp. 5772-5772
Open Access | Times Cited: 62
Utilization of model-agnostic explainable artificial intelligence frameworks in oncology: a narrative review
Colton Ladbury, Reza Zarinshenas, Hemal Semwal, et al.
Translational Cancer Research (2022) Vol. 11, Iss. 10, pp. 3853-3868
Open Access | Times Cited: 43
A Regional-Attentive Multi-Task Learning Framework for Breast Ultrasound Image Segmentation and Classification
Meng Xu, Kuan Huang, Xiaojun Qi
IEEE Access (2023) Vol. 11, pp. 5377-5392
Open Access | Times Cited: 34
A New Deep-Learning-Based Model for Breast Cancer Diagnosis from Medical Images
Salman Zakareya, Habib Izadkhah, Jaber Karimpour
Diagnostics (2023) Vol. 13, Iss. 11, pp. 1944-1944
Open Access | Times Cited: 26
Explainable machine learning for breast cancer diagnosis from mammography and ultrasound images: a systematic review
Daraje Kaba Gurmessa, Worku Jimma
BMJ Health & Care Informatics (2024) Vol. 31, Iss. 1, pp. e100954-e100954
Open Access | Times Cited: 9
Comparison of Traditional Radiomics, Deep Learning Radiomics and Fusion Methods for Axillary Lymph Node Metastasis Prediction in Breast Cancer
Xue Li, Lifeng Yang, Xiong Jiao
Academic Radiology (2022) Vol. 30, Iss. 7, pp. 1281-1287
Closed Access | Times Cited: 31
Development of High-Resolution Dedicated PET-Based Radiomics Machine Learning Model to Predict Axillary Lymph Node Status in Early-Stage Breast Cancer
Jingyi Cheng, Caiyue Ren, Guangyu Liu, et al.
Cancers (2022) Vol. 14, Iss. 4, pp. 950-950
Open Access | Times Cited: 30
Artificial Intelligence in Breast Ultrasound: From Diagnosis to Prognosis—A Rapid Review
Nicole Brunetti, Massimo Calabrese, Carlo Martinoli, et al.
Diagnostics (2022) Vol. 13, Iss. 1, pp. 58-58
Open Access | Times Cited: 29
Deep Learning in Different Ultrasound Methods for Breast Cancer, from Diagnosis to Prognosis: Current Trends, Challenges, and an Analysis
Humayra Afrin, Nicholas B. Larson, Mostafa Fatemi, et al.
Cancers (2023) Vol. 15, Iss. 12, pp. 3139-3139
Open Access | Times Cited: 16
Automated and reusable deep learning (AutoRDL) framework for predicting response to neoadjuvant chemotherapy and axillary lymph node metastasis in breast cancer using ultrasound images: a retrospective, multicentre study
Jingjing You, Yue Huang, Lizhu Ouyang, et al.
EClinicalMedicine (2024) Vol. 69, pp. 102499-102499
Open Access | Times Cited: 6
Deep learning radiomics based prediction of axillary lymph node metastasis in breast cancer
Han Liu, Liwen Zou, Nan Xu, et al.
npj Breast Cancer (2024) Vol. 10, Iss. 1
Open Access | Times Cited: 5
Presentation of Novel Architecture for Diagnosis and Identifying Breast Cancer Location Based on Ultrasound Images Using Machine Learning
Yaghoub Pourasad, Esmaeil Zarouri, Mohammad Salemizadeh Parizi, et al.
Diagnostics (2021) Vol. 11, Iss. 10, pp. 1870-1870
Open Access | Times Cited: 32
A machine learning model based on ultrasound image features to assess the risk of sentinel lymph node metastasis in breast cancer patients: Applications of scikit-learn and SHAP
Gaosen Zhang, Yan Shi, Peipei Yin, et al.
Frontiers in Oncology (2022) Vol. 12
Open Access | Times Cited: 26
Detection of Metastatic Breast Cancer from Whole-Slide Pathology Images Using an Ensemble Deep-Learning Method
Jafar Abdollahi, Niyousha Davari, Yasin Panahi, et al.
Archives of Breast Cancer (2022), pp. 364-376
Open Access | Times Cited: 23
Graph-Based Multi-Modality Network for Axillary Lymph Node Metastasis Prediction in Early-Stage Breast Cancer
Y Q Xia, Ning Qu, Yongzhong Lin, et al.
(2025)
Closed Access
Axillary lymph node metastasis in breast cancer: from historical axillary surgery to updated advances in the preoperative diagnosis and axillary management
Tong Wu, Long Qian, Liyun Zeng, et al.
BMC Surgery (2025) Vol. 25, Iss. 1
Open Access
Deep Learning: A Heuristic Three-Stage Mechanism for Grid Searches to Optimize the Future Risk Prediction of Breast Cancer Metastasis Using EHR-Based Clinical Data
Xia Jiang, Yijun Zhou, Chuhan Xu, et al.
Cancers (2025) Vol. 17, Iss. 7, pp. 1092-1092
Open Access
Axillary lymph node metastasis prediction by contrast-enhanced computed tomography images for breast cancer patients based on deep learning
Ziyi Liu, Sijie Ni, Chunmei Yang, et al.
Computers in Biology and Medicine (2021) Vol. 136, pp. 104715-104715
Closed Access | Times Cited: 27
Ultrasound radiomics in personalized breast management: Current status and future prospects
Jionghui Gu, Tianan Jiang
Frontiers in Oncology (2022) Vol. 12
Open Access | Times Cited: 20
A dual-transformation with contrastive learning framework for lymph node metastasis prediction in pancreatic cancer
Xiahan Chen, Weishen Wang, Yu Jiang, et al.
Medical Image Analysis (2023) Vol. 85, pp. 102753-102753
Closed Access | Times Cited: 9
The Role of AI in Breast Cancer Lymph Node Classification: A Comprehensive Review
Josip Vrdoljak, Ante Krešo, Marko Kumrić, et al.
Cancers (2023) Vol. 15, Iss. 8, pp. 2400-2400
Open Access | Times Cited: 9
Artificial intelligence in breast imaging: potentials and challenges
Jia-wei Li, Danli Sheng, Jiangang Chen, et al.
Physics in Medicine and Biology (2023) Vol. 68, Iss. 23, pp. 23TR01-23TR01
Open Access | Times Cited: 9
State-of-the-Art of Breast Cancer Diagnosis in Medical Images via Convolutional Neural Networks (CNNs)
Pratibha Harrison, Rakib Hasan, Kihan Park
Journal of Healthcare Informatics Research (2023) Vol. 7, Iss. 4, pp. 387-432
Closed Access | Times Cited: 8
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