Enabling clinical use of foundation models in histopathology
Abstrak
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 comprehensive experimentation setup with 27,042 WSIs from 6155 patients is used to train thousands of models from the features of eight popular foundation models for computational pathology. In addition to a substantial improvement in robustness, we observe that prediction accuracy improves by focusing on biologically relevant features. Our approach successfully mitigates robustness issues of foundation models for computational pathology without retraining the foundation models themselves, enabling development of robust computational pathology models applicable to real-world data in routine clinical practice.
Penulis (34)
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
Akses Cepat
- Tahun Terbit
- 2026
- Bahasa
- en
- Sumber Database
- arXiv
- Akses
- Open Access ✓