Swarm learning with weak supervision enables automatic breast cancer detection in magnetic resonance imaging

Combining weakly supervised learning with SL enhances inter-institutional collaboration, improving the utility of distributed datasets for medical AI training without requiring detailed annotations or centralized data sharing.

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Publication Details

Journal
Communications Medicine
Published
2025-02-06
DOI
https://doi.org/10.1038/s43856-024-00722-5
Citations
6
Primary Topic
AI in cancer detection
Type
article
Field-Weighted Citation Impact
14.89

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article

Swarm learning with weak supervision enables automatic breast cancer detection in magnetic resonance imaging

Paul-Christophe Varoutas, Alexandra Athanasiou, Kathy Pfeiffer, Daniel Truhn et al.
6 citations
Communications Medicine
AI in cancer detection
14.89
article

Swarm learning with weak supervision enables automatic breast cancer detection in magnetic resonance imaging

Paul-Christophe Varoutas, Alexandra Athanasiou, Kathy Pfeiffer, Daniel Truhn, Raquel Pérez-López, Kamarul Amin Abdullah, С. П. Морозов, Sylwia Nowakowska, Jiefu Zhu, Julia Camps Herrero, L. Escudero, Sreenath P. Kyathanahally, Wouter B. Veldhuis, Gustav Müller‐Franzes, Marta Ligero, Narmin Ghaffari Laleh, M Wenzel, Cristina Rossi, Fiona J. Gilbert, Christiane Katharina Kuhl, Zunamys I. Carrero, Volkmar Schulz, Ritse M. Mann, Jakob Nikolas Kather, Oliver Lester Saldanha, Alexander Ciritsis, Nicholas Roy Payne, Jakob Behner, Georgios Apostolakos, Chrysafoula Kolofousi, Antri Kleanthous, Michail Kalogeropoulos
article en
6 citations

Abstract

Combining weakly supervised learning with SL enhances inter-institutional collaboration, improving the utility of distributed datasets for medical AI training without requiring detailed annotations or centralized data sharing.

Communications MedicineVol. 5(1)
Radboud University Nijmegen (NL), University of Bremen (DE), Heidelberg University (DE), University of Cambridge (GB), University Hospital Heidelberg (DE), European Society of Radiology (AT), Radboud University Medical Center (NL), The Netherlands Cancer Institute (NL), University Medical Center Utrecht (NL), Cancer Research UK Cambridge Center (GB), Hospital de La Ribera (ES), University Hospital of Zurich (CH), Oncode Institute (NL), National Center for Tumor Diseases (DE), Mitera Hospital (GR), Fraunhofer Institute for Digital Medicine (DE), University Hospital Carl Gustav Carus (DE), Vall d'Hebron Institute of Oncology (ES), Sultan Zainal Abidin University (MY), Technische Universität Dresden (DE), RWTH Aachen University (DE)
National Institute for Health and Care Research, Department of Health and Social Care, European Commission, Deutscher Akademischer Austauschdienst, Bundesministerium für Bildung und Forschung, Bundesministerium für Gesundheit, Deutsche Krebshilfe, Gemeinsame Bundesausschuss, HORIZON EUROPE Framework Programme, Leeds Biomedical Research Centre
Partnerships for the goals
Openalex Percentile: Top 1%
AI in cancer detection
14.89
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