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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:

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

Showing 24 citing articles:

Prostate cancer grading framework based on deep transfer learning and Aquila optimizer
Hossam Magdy Balaha, Ahmed Osama Shaban, Eman M. El-Gendy, et al.
Neural Computing and Applications (2024) Vol. 36, Iss. 14, pp. 7877-7902
Open Access | Times Cited: 15
The Applications of Artificial Intelligence for Assessing Fall Risk: Systematic Review
Ana González-Castro, Raquel Leirós‐Rodríguez, Camino Prada‐García, et al.
Journal of Medical Internet Research (2024) Vol. 26, pp. e54934-e54934
Open Access | Times Cited: 7
Clinical Use of Molecular Biomarkers in Canine and Feline Oncology: Current and Future
Heike Aupperle‐Lellbach, Alexandra Kehl, Simone de Brot, et al.
Veterinary Sciences (2024) Vol. 11, Iss. 5, pp. 199-199
Open Access | Times Cited: 5
Development of a deep learning system for predicting biochemical recurrence in prostate cancer
Lu Cao, Ruimin He, Ao Zhang, et al.
BMC Cancer (2025) Vol. 25, Iss. 1
Open Access
Prediction of pathological grade in prostate cancer: an ensemble deep learning-based whole slide image classification model
Gamze Korkmaz Erdem, Sevinç İlhan Omurca, Esra Betul Cakir, et al.
The European Physical Journal Special Topics (2025)
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.
Prostate Cancer and Prostatic Diseases (2025)
Open Access
Highly accurate and effective deep neural networks in pathological diagnosis of prostate cancer
Chengwei Zhang, Xiubin Gao, Bo Fan, et al.
World Journal of Urology (2024) Vol. 42, Iss. 1
Closed Access | Times Cited: 2
Prostate Cancer Gleason Score Classification Using Transfer Learning Models
Mona Chavda, Sheshang Degadwala
International Journal of Scientific Research in Computer Science Engineering and Information Technology (2024) Vol. 10, Iss. 2, pp. 450-458
Open Access | Times Cited: 1
Using multi-label ensemble CNN classifiers to mitigate labelling inconsistencies in patch-level Gleason grading
Muhammad Asim Butt, Muhammad Farhat Kaleem, Muhammad Bilal, et al.
PLoS ONE (2024) Vol. 19, Iss. 7, pp. e0304847-e0304847
Open Access | Times Cited: 1
Biomarkers of prostate bladder and testicular cancers: current use in anatomic pathology and future directions
Mariana Andozia Morini, Daniel Abensur Athanazio, Luiza Fadul Gallas, et al.
Surgical and Experimental Pathology (2024) Vol. 7, Iss. 1
Open Access
Histopathological Cancer Detection Using Pre-Trained Models
Sarojini Balakrishnan, S. Vijaya Shree
(2024) Vol. III, pp. 626-629
Closed Access
Artificial intelligence in the management of prostate cancer
Raghav Khanna, Alejandro Granados, Nicholas Raison, et al.
Nature Reviews Urology (2024) Vol. 22, Iss. 3, pp. 125-126
Closed Access
Advancing prostate cancer diagnosis and treatment through pathomics and artificial intelligence
Derek J. Van Booven, Cheng-Bang Chen, Abhishek Gupta, et al.
Elsevier eBooks (2024), pp. 41-66
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.
(2024)
Open Access
Machine Learning Approaches in Virtual Biopsy: A Review of Recent Developments and Applications
Ajaz Shah, Vishwam Modi, Yogesh Kumar
(2024), pp. 1-6
Closed Access
Artificial Intelligence Applications in Prostate Cancer Management: Success Stories and Future Ahead
Raghav Khanna, Alejandro Granados Martinez, Nicholas Raison, et al.
UroCancer Clinics of India . (2024) Vol. 2, Iss. 1, pp. 50-62
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
A Microscope Setup and Methodology for Capturing Hyperspectral and RGB Histopathological Imaging Databases
Gonzalo Rosa, Manuel Villa, Sara Hiller-Vallina, et al.
Sensors (2024) Vol. 24, Iss. 17, pp. 5654-5654
Open Access
Tomorrow’s patient management: LLMs empowered by external tools
Kelvin Szolnoky, Tobias Nordström, Martin Eklund
Nature Reviews Urology (2024)
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.
medRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access
A Classifier Model Using Fine-Tuned Convolutional Neural Network and Transfer Learning Approaches for Prostate Cancer Detection
Murat Sarıateş, Erdal Özbay
Applied Sciences (2024) Vol. 15, Iss. 1, pp. 225-225
Open Access
Applications of Artificial Intelligence for assessing fall risk: A systematic review (Preprint)
Ana González-Castro, Raquel Leirós‐Rodríguez, Camino Prada‐García, et al.
(2023)
Open Access | Times Cited: 1
Using digital pathology to analyze the murine cerebrovasculature
Dana M. Niedowicz, Jenna L. Gollihue, Erica M. Weekman, et al.
Journal of Cerebral Blood Flow & Metabolism (2023) Vol. 44, Iss. 4, pp. 595-610
Open Access
A Comprehensive Review on Deep Learning Approach for Prostate Cancer Gleason Grading
Mona Chavda, Sheshang Degadwala
International Journal of Scientific Research in Computer Science Engineering and Information Technology (2023), pp. 270-275
Open Access
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