ScholarIQanswers from OpenAlex
AI in cancer detection
TopicLeading institutions, researchers & key papers
AI in cancer detection is a topic indexed in ScholarIQ from OpenAlex.
What is known about AI in cancer detection?
ScholarIQrecord summary
This cluster of papers focuses on the application of deep learning and machine learning techniques in medical image analysis, particularly in the context of histopathology images, digital pathology, and computer-aided detection for breast cancer diagnosis. The use of convolutional neural networks and whole slide imaging is prominent in these studies, aiming to improve accuracy and efficiency in cancer prognosis and prediction.
How many works does AI in cancer detection have?
ScholarIQindexed works
AI in cancer detection has 487 works in the ScholarIQ index. The count is the OpenAlex total, not the number of papers listed on this page.
What is the OpenAlex record for AI in cancer detection?
ScholarIQopenalex
The OpenAlex for AI in cancer detection is on the source record.
What are the most-cited papers on AI in cancer detection?
ScholarIQmost cited works
International evaluation of an AI system for breast cancer screening
Scott Mayer McKinney, Marcin Sieniek, Varun Godbole, Jonathan Godwin, Н. В. Антропова, Hutan Ashrafian, Trevor Back, Mary Chesus, Greg S. Corrado, Ara Darzi, Mozziyar Etemadi, Florencia Garcia-Vicente, Fiona J. Gilbert, Mark Halling‐Brown, Demis Hassabis, Sunny Jansen, Alan Karthikesalingam, Christopher Kelly, Dominic King, Joseph R. Ledsam, David Melnick, Hormuz Mostofi, Lily Peng, Joshua Reicher, Bernardino Romera‐Paredes, Richard Sidebottom, Mustafa Suleyman, Daniel Tse, Kenneth C. Young, Jeffrey De Fauw, Shravya Shetty
Clinical-grade computational pathology using weakly supervised deep learning on whole slide images
Gabriele Campanella, Matthew G. Hanna, Luke Geneslaw, Allen P. Miraflor, Vitor Werneck Krauss Silva, Klaus J. Busam, Edi Brogi, Victor E. Reuter, David S. Klimstra, Thomas J. Fuchs
Transformer-based unsupervised contrastive learning for histopathological image classification
Xiyue Wang, Sen Yang, Jun Zhang, Minghui Wang, Jing Zhang, Wei Yang, Junzhou Huang, Xiao Han
Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography: Comparison With 101 Radiologists
Alejandro Rodríguez‐Ruiz, Kristina Lång, Albert Gubern‐Mérida, Mireille J. M. Broeders, Gisella Gennaro, Paola Clauser, Thomas H. Helbich, Margarita Chevalier, Tao Tan, Thomas Mertelmeier, Matthew Wallis, Ingvar Andersson, Sophia Zackrisson, Ritse M. Mann, Ioannis Sechopoulos
National Performance Benchmarks for Modern Screening Digital Mammography: Update from the Breast Cancer Surveillance Consortium
Constance D. Lehman, Robert F. Arao, Brian L. Sprague, Janie M. Lee, Diana S.M. Buist, Karla Kerlikowske, Louise M. Henderson, Tracy Onega, Anna N.A. Tosteson, Garth H. Rauscher, Diana L. Miglioretti
Where is AI in cancer detection research published, and who funds it?
ScholarIQvenues & funding sources
TOP JOURNALS
TOP FUNDERS
National Science Foundation—
NIH—
Wellcome Trust—
European Research Council—
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How much of the research on AI in cancer detection is open access?
ScholarIQopen access share
80%OPEN ACCESS