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[2602.22347] Enabling clinical use of foundation models in histopathology
Summary
This article discusses the application of foundation models in histopathology, highlighting a novel approach that improves robustness and accuracy in computational pathology without retraining existing models.
Why It Matters
As foundation models gain traction in medical applications, ensuring their reliability in clinical settings is crucial. This research addresses the challenge of technical variability in predictions, making it relevant for enhancing diagnostic accuracy in pathology.
Key Takeaways
- Foundation models can enhance deep learning systems in histopathology.
- Introducing robustness losses during training mitigates technical variability.
- The study utilized a comprehensive dataset of 27,042 whole slide images.
- Focusing on biologically relevant features improves prediction accuracy.
- The approach allows for robust models applicable in routine clinical practice.
Computer Science > Computer Vision and Pattern Recognition arXiv:2602.22347 (cs) [Submitted on 25 Feb 2026] Title:Enabling clinical use of foundation models in histopathology Authors:Audun L. Henriksen, Ole-Johan Skrede, Lisa van der Schee, Enric Domingo, Sepp De Raedt, Ilyá Kostolomov, Jennifer Hay, Karolina Cyll, Wanja Kildal, Joakim Kalsnes, Robert W. Williams, Manohar Pradhan, John Arne Nesheim, Hanne A. Askautrud, Maria X. Isaksen, Karmele Saez de Gordoa, Miriam Cuatrecasas, Joanne Edwards, TransSCOT group, Arild Nesbakken, Neil A. Shepherd, Ian Tomlinson, Daniel-Christoph Wagner, Rachel S. Kerr, Tarjei Sveinsgjerd Hveem, Knut Liestøl, Yoshiaki Nakamura, Marco Novelli, Masaaki Miyo, Sebastian Foersch, David N. Church, Miangela M. Lacle, David J. Kerr, Andreas Kleppe View a PDF of the paper titled Enabling clinical use of foundation models in histopathology, by Audun L. Henriksen and 33 other authors View PDF HTML (experimental) Abstract:Foundation models in histopathology are expected to facilitate the development of high-performing and generalisable deep learning systems. However, current models capture not only biologically relevant features, but also pre-analytic and scanner-specific variation that bias the predictions of task-specific models trained from the foundation model features. Here we show that introducing novel robustness losses during training of downstream task-specific models reduces sensitivity to technical variability. A purpose-designed comprehensiv...