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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 detection of melanoma metastases in lymph nodes
Philipp Jansen, Daniel Otero Baguer, Nicole Duschner, et al.
European Journal of Cancer (2023) Vol. 188, pp. 161-170
Closed Access | Times Cited: 15

Showing 15 citing articles:

Pros and cons of artificial intelligence implementation in diagnostic pathology
P. J. van Diest, Rachel N Flach, Carmen van Dooijeweert, et al.
Histopathology (2024) Vol. 84, Iss. 6, pp. 924-934
Open Access | Times Cited: 15
Principles, applications, and future of artificial intelligence in dermatology
Jesutofunmi A. Omiye, Haiwen Gui, Roxana Daneshjou, et al.
Frontiers in Medicine (2023) Vol. 10
Open Access | Times Cited: 22
Deep learning model shows pathologist-level detection of sentinel node metastasis of melanoma and intra-nodal nevi on whole slide images
Jan Siarov, Angelica Siarov, Darshan Kumar, et al.
Frontiers in Medicine (2024) Vol. 11
Open Access | Times Cited: 4
Stand der Digitalisierung in der Dermatopathologie
Stephan A. Braun, Paul Schmidle, Nicole Duschner, et al.
Deleted Journal (2025)
Closed Access
Artificial Intelligence and Convolutional Neural Networks–Driven Detection of Micro and Macro Metastasis of Cutaneous Melanoma to the Lymph Nodes
Nada Shaker, Shuo Niu, Heath Blankenship, et al.
American Journal of Dermatopathology (2025)
Closed Access
Advancements in computer vision and pathology: Unraveling the potential of artificial intelligence for precision diagnosis and beyond
Justin Chang, Bryce Hatfield
Advances in cancer research (2024), pp. 431-478
Closed Access | Times Cited: 3
Predicting metastatic potential of primary cutaneous melanomas utilizing weakly supervised vision language model
Filip Dahlén, Ivan Shujski, Filmon Yacob, et al.
Research Square (Research Square) (2025)
Closed Access
Advancements and implications of artificial intelligence for early detection, diagnosis and tailored treatment of cancer
Sonia Chadha, Sayali Mukherjee, Somali Sanyal
Seminars in Oncology (2025) Vol. 52, Iss. 3, pp. 152349-152349
Closed Access
An artificial intelligence model for detecting pathological lymph node metastasis in prostate cancer using whole slide images: a retrospective, multicentre, diagnostic study
Shaoxu Wu, Yun Wang, Guibin Hong, et al.
EClinicalMedicine (2024) Vol. 71, pp. 102580-102580
Open Access | Times Cited: 2
Computational methods for metastasis detection in lymph nodes and characterization of the metastasis-free lymph node microarchitecture: A systematic-narrative hybrid review
Elzbieta Budginaite, Derek Magee, Maximilian Kloft, et al.
Journal of Pathology Informatics (2024) Vol. 15, pp. 100367-100367
Open Access | Times Cited: 1
Artificial Intelligence in Cutaneous Lesions: Where do we Stand and What is Next?
Mara Giavina‐Bianchi, Justin Ko, Mara Giavina, et al.
Frontiers research topics (2024)
Open Access | Times Cited: 1
Deep learning based histological classification of adnex tumors
Philipp Jansen, Jean Le’Clerc Arrastia, Daniel Otero Baguer, et al.
European Journal of Cancer (2023) Vol. 196, pp. 113431-113431
Closed Access | Times Cited: 2
Artificial Intelligence-Based Sentinel Lymph Node Metastasis Detection in Cervical Cancer
Ilse Baeten, Jacob P. Hoogendam, Nikolas Stathonikos, et al.
Cancers (2024) Vol. 16, Iss. 21, pp. 3619-3619
Open Access
POSSIBILITIES OF APPLYING ARTIFICIAL INTELLIGENCE IN PATHOMORPHOLOGICAL DIAGNOSIS OF SKIN DISEASES
Nikolay Karnaukhov, K.S. Korchagina, D.A. Synkova
Современные проблемы науки и образования (Modern Problems of Science and Education) (2024), Iss. №3 2024, pp. 92-92
Open Access
Human–machine interaction in computational cancer pathology
Antonia Syrnioti, António Polónia, João Correia Pinto, et al.
ESMO Real World Data and Digital Oncology (2024) Vol. 5, pp. 100062-100062
Closed Access
The use of a deep learning model in the histopathological diagnosis of actinic keratosis: A case control accuracy study
Johannes Balkenhol, Maximilian Schmidt, T. Schnauder, et al.
medRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access
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