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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:

The pathological risk score: A new deep learning‐based signature for predicting survival in cervical cancer
Chi Chen, Yuye Cao, Weili Li, et al.
Cancer Medicine (2022) Vol. 12, Iss. 2, pp. 1051-1063
Open Access | Times Cited: 22

Showing 22 citing articles:

Role of artificial intelligence in digital pathology for gynecological cancers
Yali Wang, Song Gao, Qian Xiao, et al.
Computational and Structural Biotechnology Journal (2024) Vol. 24, pp. 205-212
Open Access | Times Cited: 14
Improving prediction of cervical cancer using KNN imputer and multi-model ensemble learning
Turki Aljrees
PLoS ONE (2024) Vol. 19, Iss. 1, pp. e0295632-e0295632
Open Access | Times Cited: 11
Gynecological cancer prognosis using machine learning techniques: A systematic review of the last three decades (1990–2022)
Joshua Sheehy, Hamish Rutledge, U. Rajendra Acharya, et al.
Artificial Intelligence in Medicine (2023) Vol. 139, pp. 102536-102536
Closed Access | Times Cited: 18
Cervical cancer survival prediction by machine learning algorithms: a systematic review
Milad Rahimi, Atieh Akbari, Farkhondeh Asadi, et al.
BMC Cancer (2023) Vol. 23, Iss. 1
Open Access | Times Cited: 17
Recent Advancements in Deep Learning Using Whole Slide Imaging for Cancer Prognosis
Minhyeok Lee
Bioengineering (2023) Vol. 10, Iss. 8, pp. 897-897
Open Access | Times Cited: 14
What benefit can be obtained from magnetic resonance imaging diagnosis with artificial intelligence in prostate cancer compared with clinical assessments?
Litao Zhao, Zhenyu Liu, Wanfang Xie, et al.
Military Medical Research (2023) Vol. 10, Iss. 1
Open Access | Times Cited: 11
A quantitative analysis of artificial intelligence research in cervical cancer: a bibliometric approach utilizing CiteSpace and VOSviewer
Ziqi Zhao, Boqian Hu, Kun Xu, et al.
Frontiers in Oncology (2024) Vol. 14
Open Access | Times Cited: 4
Computational pathology: A comprehensive review of recent developments in digital and intelligent pathology
Qinqin Huang, Sean M. Wu, Zhenyu Ou, et al.
(2025)
Closed Access
An Overview of Artificial Intelligence in Gynaecological Pathology Diagnostics
Anthony M. Joshua, Katie E. Allen, Nicolas M. Orsi
Cancers (2025) Vol. 17, Iss. 8, pp. 1343-1343
Open Access
Construction and validation of a deep learning prognostic model based on digital pathology images of stage III colorectal cancer
Xuezhi Zhou, Yizhan Lu, Yue Wu, et al.
European Journal of Surgical Oncology (2024) Vol. 50, Iss. 7, pp. 108369-108369
Closed Access | Times Cited: 3
ZC3H13 Enhances the Malignancy of Cervical Cancer by Regulating m6A Modification of CKAP2
Yuan Zhang, Xiaohong Chen, Huiqun Chen, et al.
Critical Reviews in Immunology (2023) Vol. 43, Iss. 6, pp. 1-13
Closed Access | Times Cited: 8
A nomogram prediction model of pseudomyxoma peritonei established based on new prognostic factors of HE stained pathological images analysis
Ru Ma, Yan‐Dong Su, Fengcai Yan, et al.
Cancer Medicine (2024) Vol. 13, Iss. 6
Open Access | Times Cited: 2
Deep Learning Analysis for Predicting Tumor Spread through Air Space in Early-Stage Lung Adenocarcinoma Pathology Images
De-Xiang Ou, Chao-Wen Lu, Li-Wei Chen, et al.
Cancers (2024) Vol. 16, Iss. 11, pp. 2132-2132
Open Access | Times Cited: 2
Computer-aided detection and prognosis of colorectal cancer on whole slide images using dual resolution deep learning
Yan Xu, Liwen Jiang, Wenjing Chen, et al.
Journal of Cancer Research and Clinical Oncology (2022) Vol. 149, Iss. 1, pp. 91-101
Closed Access | Times Cited: 5
Case-Base Neural Network: Survival analysis with time-varying, higher-order interactions
Jesse Islam, Maxime Turgeon, Robert Sladek, et al.
Machine Learning with Applications (2024) Vol. 16, pp. 100535-100535
Open Access
Prognostic Factors for Cervical Cancer in Asian Populations: A Scoping Review of Research From 2013 to 2023
Syed S Abrar, Seoparjoo Azmel Mohd Isa, Suhaily Mohd Hairon, et al.
Cureus (2024)
Open Access
Deep weighted survival neural networks to survival risk prediction
Yu Hui, Qingyong Wang, Xiaobo Zhou, et al.
Complex & Intelligent Systems (2024) Vol. 11, Iss. 1
Open Access
HiAt-Net: A Novel Three-Layered Encoder-Decoder with Hierarchical Attention for Classifying Cervical Cancer with Cytology Images
Rakesh Kumar Mahendran, Parthasarathy Ramadass, M. Kiruthiga Devi, et al.
(2024), pp. 1-10
Closed Access
Case-Base Neural Networks: survival analysis with time-varying, higher-order interactions
Jesse Islam, Maxime Turgeon, Robert Sladek, et al.
arXiv (Cornell University) (2023)
Open Access
Research Progress on Application of Artificial Intelligence in Gynecological Malignant Tumor
紫均 陈
Advances in Clinical Medicine (2023) Vol. 13, Iss. 06, pp. 10542-10548
Closed Access
Segmenting Cervical Arteries in Phase Contrast Magnetic Resonance Imaging Using Convolutional Encoder–Decoder Networks
Britney Campbell, Dhruv Yadav, Ramy Hussein, et al.
Applied Sciences (2023) Vol. 13, Iss. 21, pp. 11820-11820
Open Access
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