Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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:

Unsupervised Resolution of Histomorphologic Heterogeneity in Renal Cell Carcinoma Using a Brain Tumor–Educated Neural Network
Kevin Faust, Adil Roohi, Alberto J. León, et al.
JCO Clinical Cancer Informatics (2020), Iss. 4, pp. 811-821
Open Access | Times Cited: 27

Showing 1-25 of 27 citing articles:

PHARAOH: A collaborative crowdsourcing platform for phenotyping and regional analysis of histology
Kevin Faust, Min Li Chen, Parsa Babaei Zadeh, et al.
Nature Communications (2025) Vol. 16, Iss. 1
Open Access | Times Cited: 1
Deep learning can predict survival directly from histology in clear cell renal cell carcinoma
Frederik Wessels, Max Schmitt, Eva Krieghoff‐Henning, et al.
PLoS ONE (2022) Vol. 17, Iss. 8, pp. e0272656-e0272656
Open Access | Times Cited: 29
Searching Images for Consensus
Hamid R. Tizhoosh, Phedias Diamandis, Clinton J.V. Campbell, et al.
American Journal Of Pathology (2021) Vol. 191, Iss. 10, pp. 1702-1708
Open Access | Times Cited: 38
Intratumoral Resolution of Driver Gene Mutation Heterogeneity in Renal Cancer Using Deep Learning
Paul H. Acosta, Vandana Panwar, Vipul Jarmale, et al.
Cancer Research (2022) Vol. 82, Iss. 15, pp. 2792-2806
Open Access | Times Cited: 22
Deep learning features encode interpretable morphologies within histological images
Ali Foroughi pour, Brian S. White, Jonghanne Park, et al.
Scientific Reports (2022) Vol. 12, Iss. 1
Open Access | Times Cited: 21
Multi‐Omics‐Based Autophagy‐Related Untypical Subtypes in Patients with Cerebral Amyloid Pathology
Jong‐Chan Park, Natalia Barahona‐Torres, So‐Yeong Jang, et al.
Advanced Science (2022) Vol. 9, Iss. 23
Open Access | Times Cited: 19
Brain Tumor MRI Segmentation Method Based on Improved Res-UNet
Xue Li, Zhenqi Fang, Ruhua Zhao, et al.
IEEE Journal of Radio Frequency Identification (2024) Vol. 8, pp. 652-657
Closed Access | Times Cited: 4
Digital pathology and artificial intelligence in renal cell carcinoma focusing on feature extraction: a literature review
Mingyue Li, Yu Pan, Yang Lv, et al.
Frontiers in Oncology (2025) Vol. 15
Open Access
Predicting response to neoadjuvant chemotherapy in muscle-invasive bladder cancer via interpretable multimodal deep learning
Zilong Bai, Mohamed Osman, Matthew Brendel, et al.
npj Digital Medicine (2025) Vol. 8, Iss. 1
Open Access
Artery segmentation and atherosclerotic plaque quantification using AI for murine whole slide images stained with oil red O
Johann Christopher Engster, Tobias Reinberger, Nele Blum, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
Artificial intelligence to predict oncological outcome directly from hematoxylin and eosin-stained slides in urology
Frederik Wessels, Sara Kuntz, Eva Krieghoff‐Henning, et al.
Minerva Urology and Nephrology (2022) Vol. 74, Iss. 5
Closed Access | Times Cited: 17
Artificial Intelligence in Renal Cell Carcinoma Histopathology: Current Applications and Future Perspectives
Alfredo Distante, Laura Marandino, Riccardo Bertolo, et al.
Diagnostics (2023) Vol. 13, Iss. 13, pp. 2294-2294
Open Access | Times Cited: 8
Empowering Renal Cancer Management with AI and Digital Pathology: Pathology, Diagnostics and Prognosis
Елена Иванова, Alexey Fayzullin, Victor Grinin, et al.
Biomedicines (2023) Vol. 11, Iss. 11, pp. 2875-2875
Open Access | Times Cited: 7
Integrating computational pathology and proteomics to address tumor heterogeneity
Anglin Dent, Phedias Diamandis
The Journal of Pathology (2022) Vol. 257, Iss. 4, pp. 445-453
Closed Access | Times Cited: 11
Clinical Application of Digital and Computational Pathology in Renal Cell Carcinoma: A Systematic Review
Z. Khene, Solène‐Florence Kammerer‐Jacquet, Pierre Bigot, et al.
European Urology Oncology (2023) Vol. 7, Iss. 3, pp. 401-411
Closed Access | Times Cited: 6
Selection, Visualization, and Interpretation of Deep Features in Lung Adenocarcinoma and Squamous Cell Carcinoma
Taher Dehkharghanian, Shahryar Rahnamayan, Abtin Riasatian, et al.
American Journal Of Pathology (2021) Vol. 191, Iss. 12, pp. 2172-2183
Open Access | Times Cited: 14
Integrating morphologic and molecular histopathological features through whole slide image registration and deep learning
Kevin Faust, Michael Kyung Ik Lee, Anglin Dent, et al.
Neuro-Oncology Advances (2022) Vol. 4, Iss. 1
Open Access | Times Cited: 9
Deep Learning for Image Analysis in Kidney Care
Hanjie Zhang, Max Botler, Jeroen P. Kooman
Advances in Kidney Disease and Health (2022) Vol. 30, Iss. 1, pp. 25-32
Open Access | Times Cited: 9
Artificial Intelligence in Renal Cell Carcinoma Histopathology: Current Applications and Future Perspectives
Alfredo Distante, Laura Marandino, Riccardo Bertolo, et al.
(2023)
Open Access | Times Cited: 3
HAVOC: Small-scale histomic mapping of cancer biodiversity across large tissue distances using deep neural networks
Anglin Dent, Kevin Faust, K. H. Brian Lam, et al.
Science Advances (2023) Vol. 9, Iss. 39
Open Access | Times Cited: 3
MRI Image Segmentation of Brain Tumor Based on Res-UNet
Xue Li, Zhenqi Fang, Ruhua Zhao, et al.
(2023), pp. 1-6
Closed Access | Times Cited: 2
Incorporating Intratumoral Heterogeneity into Weakly-Supervised Deep Learning Models via Variance Pooling
Iain Carmichael, Andrew H. Song, Richard J. Chen, et al.
Lecture notes in computer science (2022), pp. 387-397
Closed Access | Times Cited: 4
Cancer Data Science and Computational Medicine
Peter Paul Yu, Warren A. Kibbe
JCO Clinical Cancer Informatics (2021), Iss. 5, pp. 487-489
Closed Access | Times Cited: 5
The expanding role of artificial intelligence in the histopathological diagnosis in urological oncology: a literature review
Jasmin Gurung, Mladen Doykov, Gancho Kostov, et al.
Folia Medica (2024) Vol. 66, Iss. 3, pp. 303-311
Open Access
Renal cell carcinoma therapeutics guided by artificial intelligence methods
Z. Khene, Yair Lotan, Vitaly Margulis, et al.
Elsevier eBooks (2024), pp. 103-114
Closed Access
Page 1 - Next Page

Scroll to top