Real-world benchmarking and validation of foundation model transformers for endometrial cancer subtyping from histopathology

Abstract We benchmarked histopathology foundation encoders paired with attention-based multiple instance learning (MIL) against convolutional neural networks (CNNs) to assess their robustness for endometrial cancer molecular classification (MMR-deficient, p53 aberrant, POLE pathogenic mutation, and...

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Auteurs principaux:	Vincent M. Wagner, Casey M. Cosgrove, Stephanie J. Chen, Daniel T. Griffin, Megan I. Samuelson, Michael J. Goodheart, Jesus Gonzalez-Bosquet
Format:	Artigo
Langue:	Inglês
Publié:	Nature Portfolio 2026-04-01
Collection:	npj Precision Oncology
Accès en ligne:	https://doi.org/10.1038/s41698-026-01402-4
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