ALICE unifies vision and slide-level expertise via agglomerative distillation

Researchers present ALICE, a unified foundation model for computational pathology trained through multi-stage agglomerative distillation. The model sequentially consolidates capabilities from eight specialized teacher models into a single backbone.

• Trained on 24,985,184 tile-level pathology images and 155,604 high-resolution images.
• Evaluated across 21 task scenarios, 96 downstream tasks, and 48 data sources.
• Achieved the best average rank among task-matched pathology foundation models in all three evaluation settings.

The results demonstrate that agglomerative distillation can consolidate complementary capabilities from specialized models into a unified backbone for broad computational pathology applications.