ALICE用聚合蒸馏把8个专家模型塞进一个骨干，在96个病理任务上拿了最佳平均分，还开源了代码。
ALICE是一个通过多阶段聚合蒸馏训练的统一基础模型，从8个视觉、视觉语言和切片级教师模型中蒸馏知识到单个骨干。预训练数据包括24,985,184张tile级病理图像和155,604张高分辨率图像，在21个任务场景、96个下游任务、48个数据源上评估。在三类评估（组织感兴趣区域分析、视觉语言多模态评估、全切片临床评估）中，ALICE均取得任务匹配病理学基础模型的最佳平均排名。模型代码已在GitHub开源。
ALICE: Learning a General-Purpose Pathology Foundation Model from Vision, Vision-Language, and Slide-Level Experts
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.