{"ID":6497625,"CreatedAt":"2026-07-13T01:19:40.13847098Z","UpdatedAt":"2026-07-14T01:36:59.12045529Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.09526","arxiv_id":"2607.09526","title":"ALICE: Learning a General-Purpose Pathology Foundation Model from Vision, Vision-Language, and Slide-Level Experts","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.","short_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 distillati...","url_abs":"https://arxiv.org/abs/2607.09526","url_pdf":"https://arxiv.org/pdf/2607.09526v1","authors":"[\"Jiawen Li\",\"Tian Guan\",\"Huijuan Shi\",\"Xitong Ling\",\"Mingxi Fu\",\"Anjia Han\",\"Chao He\",\"Yonghong He\"]","published":"2026-07-10T15:35:06Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\"]","methods":"[]","has_code":false,"code_links":[{"ID":614101,"CreatedAt":"2026-07-13T01:19:40.13847098Z","UpdatedAt":"2026-07-13T01:19:40.13847098Z","DeletedAt":null,"paper_id":6497625,"paper_url":"https://arxiv.org/abs/2607.09526","paper_title":"ALICE: Learning a General-Purpose Pathology Foundation Model from Vision, Vision-Language, and Slide-Level Experts","repo_url":"https://github.com/WonderLandxD/ALICE","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}