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

• URL: http://arxiv.org/abs/2607.09526v1
• Date: Fri, 10 Jul 2026 15:35:06 GMT
• Title: ALICE: Learning a General-Purpose Pathology Foundation Model from Vision, Vision-Language, and Slide-Level Experts
• Abstract summary: ALICE is a unified foundation model trained through multi-stage agglomerative distillation.<n>ALICE is pretrained on 24,985,184 tile-level pathology images and 155,604 high-resolution images.<n>ALICE achieved the best average rank among task-matched pathology foundation models.
• Score: 9.6920380318308
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Foundation models are reshaping computational pathology, yet their capabilities remain shaped by pretraining objectives, data sources, and spatial scales, fragmenting complementary expertise across separate backbones. Here we present ALICE, a unified foundation model trained through multi-stage agglomerative distillation that sequentially distills eight vision-only, vision-language, and slide-level teacher models into dedicated modules of a single backbone. ALICE is pretrained on 24,985,184 tile-level pathology images and 155,604 high-resolution images, and evaluated across 21 task scenarios, 96 downstream tasks, and 48 data sources, spanning region-of-interest tissue analysis, vision-language multimodal evaluation, and whole-slide clinical assessment. In all three evaluation settings, ALICE achieved the best average rank among task-matched pathology foundation models. These results demonstrate that agglomerative distillation can consolidate complementary capabilities from specialized models into a unified backbone for broad computational pathology applications. The model is available at https://github.com/WonderLandxD/ALICE.

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