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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:

A comparative study of the inter-observer variability on Gleason grading against Deep Learning-based approaches for prostate cancer
Jose Manuel Marron-Esquivel, Lourdes Durán-López, A. Linares-Barranco, et al.
Computers in Biology and Medicine (2023) Vol. 159, pp. 106856-106856
Open Access | Times Cited: 18

Showing 18 citing articles:

Cancer detection and segmentation using machine learning and deep learning techniques: a review
Hari Mohan
Multimedia Tools and Applications (2023) Vol. 83, Iss. 9, pp. 27001-27035
Closed Access | Times Cited: 31
Deep Learning Methodologies Applied to Digital Pathology in Prostate Cancer: A Systematic Review
Noémie Rabilloud, Pierre Allaume, Oscar Acosta, et al.
Diagnostics (2023) Vol. 13, Iss. 16, pp. 2676-2676
Open Access | Times Cited: 24
Pronostic de l’adénocarcinome prostatique de score de Gleason 7 (3 + 4) comportant un faible taux de grade 4 - Étude rétrospective de 104 cas
Guillaume Abitbol, T. Forzini, Pedro Sousa, et al.
Annales de Pathologie (2025)
Closed Access
Automating Prostate Cancer Grading: A Novel Deep Learning Framework for Automatic Prostate Cancer Grade Assessment using Classification and Segmentation
Saidul Kabir, Rusab Sarmun, Rafif Mahmood Al Saady, et al.
Deleted Journal (2025)
Closed Access
Enhancing histopathological image analysis: An explainable vision transformer approach with comprehensive interpretation methods and evaluation of explanation quality
Aqib Nazir Mir, Danish Raza Rizvi, Munir Ahmad
Engineering Applications of Artificial Intelligence (2025) Vol. 149, pp. 110519-110519
Closed Access
A generalised vision transformer-based self-supervised model for diagnosing and grading prostate cancer using histological images
Abadh K. Chaurasia, H Harris, Patrick W Toohey, et al.
Prostate Cancer and Prostatic Diseases (2025)
Open Access
A selective CutMix approach improves generalizability of deep learning-based grading and risk assessment of prostate cancer
Sushant Patkar, Stephanie A. Harmon, Isabell A. Sesterhenn, et al.
Journal of Pathology Informatics (2024) Vol. 15, pp. 100381-100381
Open Access | Times Cited: 3
Supervised machine learning: A new method to predict the outcomes following exercise intervention in children with autism spectrum disorder
Zhiyuan Sun, Yunhao Yuan, Xiaoxiao Dong, et al.
International Journal of Clinical and Health Psychology (2023) Vol. 23, Iss. 4, pp. 100409-100409
Open Access | Times Cited: 9
Early Detection of Prostate Cancer: The Role of Scent
Fabio Grizzi, Carmen Bax, Mohamed A.A.A. Hegazi, et al.
Chemosensors (2023) Vol. 11, Iss. 7, pp. 356-356
Open Access | Times Cited: 4
Predictive Deep Learning: An Analysis of Inception V3, VGG16, and VGG19 Models for Breast Cancer Detection
Kanika Kansal, Sanjiv Sharma
Communications in computer and information science (2024), pp. 347-357
Closed Access
Molecular landscape for risk prediction and personalized therapeutics of castration-resistant prostate cancer: at a glance
Jingang Jian, Xinan Wang, Jun Zhang, et al.
Frontiers in Endocrinology (2024) Vol. 15
Open Access
Study and Analysis of the Heterogeneity of a Prostate Cancer Dataset: First Steps on the Release of a Multicenter Strongly-Annotated Dataset
Jose Manuel Marron-Esquivel, Lourdes Durán-López, Juan P. Dominguez‐Morales
Springer Proceedings in Materials (2024), pp. 414-423
Closed Access
Artificial Intelligence Algorithms and Their Current Role in the Identification and Comparison of Gleason Patterns in Prostate Cancer Histopathology: A Comprehensive Review
Usman Khalid, Jasmin Gurung, Mladen Doykov, et al.
Diagnostics (2024) Vol. 14, Iss. 19, pp. 2127-2127
Open Access
The CrowdGleason dataset: Learning the Gleason grade from crowds and experts
Miguel López-Pérez, Alba Morquecho, Arne Schmidt, et al.
Computer Methods and Programs in Biomedicine (2024) Vol. 257, pp. 108472-108472
Open Access
A Generalised Vision Transformer-Based Self-Supervised Model for Diagnosing and Grading Prostate Cancer Using Histological Images
Abadh K. Chaurasia, H Harris, Patrick W Toohey, et al.
(2024)
Open Access
Prostate Cancer Gleason Grading: A Review on Deep Learning Approaches for Recognizing
Maulika Patel, Parag Sanghani, Niraj Shah
ITM Web of Conferences (2024) Vol. 65, pp. 03013-03013
Open Access
Transfer learning classification of suspicious lesions on breast ultrasound: is there room to avoid biopsies of benign lesions?
P. De Marco, Vincenzo Ricciardi, Marta Montesano, et al.
European Radiology Experimental (2024) Vol. 8, Iss. 1
Open Access
A generalised vision transformer-based self-supervised model for diagnosing and grading prostate cancer using histological images
Abadh K. Chaurasia, H Harris, Patrick W Toohey, et al.
medRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access
Assessing the performance of fully supervised and weakly supervised learning in breast cancer histopathology
Huan Kang, Qianqian Xu, Duofang Chen, et al.
Expert Systems with Applications (2023) Vol. 237, pp. 121575-121575
Closed Access | Times Cited: 1
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