Jessica Loo, Theodoros Pissas, Byunghak Kang, Vincent A. Ella, Xiaoyan Zhang, Kevin Sweeney, Walter Georgescu, Prateek Katiyar · 2026 · arXiv
DOI: 10.48550/arXiv.2610.26586 · open-access PDF
Aquí, presentamos TOX-FM, una familia de PFMs no clínicos entrenados en conjuntos de datos de histopatología diversa a gran escala de más de un millón de imágenes de diapositivas completas compuestas de más de 60 órganos en seis especies animales de más de 14.000 estudios de toxicología a lo largo de seis décadas. Introducimos un benchmark integral de 18 tareas descendentes no clínicas, incorporamos un benchmark establecido de 9 tareas descendentes clínicas, y evaluamos TOX-FMs contra PFMs clínicos de última generación en ambos dominios no clínicos y clínicos.
Machine translation (Meta M2M100); the English original follows.
Here, we present TOX-FM, a family of non-clinical PFMs trained on large-scale diverse histopathology datasets of over one million whole slide images consisting of over 60 organs across six animal species from over 14,000 toxicology studies across six decades. We introduce a comprehensive benchmark of 18 non-clinical downstream tasks, incorporate an established benchmark of 9 clinical downstream tasks, and evaluate TOX-FM against state-of-the-art clinical PFMs across both non-clinical and clinical domains. Notably, we explore open questions surrounding domain-specific training and cross-domain
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