ALICE: Learning a General-Purpose Pathology Foundation Model from Vision, Vision-Language, and Slide-Level Experts

Pathology foundation models are typically trained with narrow objectives on limited data scales, fragmenting complementary strengths across separate models. This paper presents a unified pathology foundation model built through staged distillation, combining knowledge from eight vision-only, vision-language, and slide-level teacher models into one backbone, trained on nearly 25 million pathology images. Evaluated across 96 downstream tasks and 48 data sources, it achieves top average performance across tissue-level, multimodal, and whole-slide clinical tasks. Applications include a single, versatile AI backbone for computational pathology supporting cancer diagnosis, tissue analysis, and clinical decision support across diverse tasks and institutions. Authors: Jiawen Li, Tian Guan, Huijuan Shi, Xitong Ling, Mingxi Fu, Anjia Han, Chao He, Yonghong He Paper: https://arxiv.org/abs/2607.09526v1