ALICE: Learning a General-Purpose Pathology Foundation Model from Vision, Vision-Language, and Slide-Level Experts
ALICE is a unified foundation model for computational pathology, trained through multi-stage agglomerative distillation from vision, vision-language, and slide-level experts. The paper is published on arXiv.
ALICE is a general-purpose pathology foundation model that unifies expertise from vision, vision-language, and slide-level experts via multi-stage agglomerative distillation.
The multi-stage agglomerative distillation approach may enable ALICE to integrate complementary expertise across different spatial scales and pretraining objectives, potentially outperforming single-backbone models.
This work addresses the fragmentation of foundation models in computational pathology by unifying multiple expert backbones, which could streamline deployment in clinical workflows.
If validated, ALICE could reduce the need for multiple specialized models in pathology labs, lowering integration costs and improving diagnostic consistency.
Future work may benchmark ALICE against existing pathology foundation models on downstream tasks such as diagnosis and prognosis. Next signal: publication of benchmark results or open-source release.