Newest papers

Scaling Vision Foundation Models for Toxicologic Pathology

Jessica Loo, Theodoros Pissas, Byunghak Kang, Vincent A. Ella, Xiaoyan Zhang, Kevin Sweeney, Walter Georgescu, Prateek Katiyar, Julie Boisclair, Holger Hoefling · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.26586 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

ここでは、TOX-FMは、6十年間にわたる14000以上の毒性学研究から60以上の臓器を含む600万以上のスライド画像を含む大規模な多様なヒストパトロジーデータセットに訓練された非臨床PFMの家族です。我々は、18の非臨床下流任務の包括的なベンチマークを導入し、9つの臨床下流任務の既定のベンチマークを組み込み、非臨床および臨床分野の両方で最先端の臨床PFMと対してTOX-FMを評価します。

Machine translation (Meta M2M100); the English original follows.

Abstract

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 generalization, offering insights into the opportunities and challenges of applying PFMs in the non-clinical domain.

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Topics: Benchmark (surveying), Foundation (evidence), Computer science, Downstream (manufacturing), Engineering, Medical physics

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