ALICE：从视觉、视觉语言和玻片级专家学习通用病理学基础模型

中文摘要 AI 辅助

基础模型正在重塑计算病理学，但其能力仍受预训练目标、数据源和空间尺度的限制，将互补的专业知识分散在不同的主干中。本文提出了ALICE，这是一个通过多阶段凝聚蒸馏训练的统一基础模型，它将八个仅视觉、视觉语言和玻片级教师模型依次蒸馏到单个主干的专用模块中。ALICE在24,985,184个瓦片级病理图像和155,604个高分辨率图像上进行预训练，并在21个任务场景、96个下游任务和48个数据源上进行评估，涵盖感兴趣区域组织分析、视觉语言多模态评估和全玻片临床评估。在所有三种评估设置中，ALICE在任务匹配的病理基础模型中获得了最佳平均排名。这些结果表明，凝聚蒸馏可以将专门模型的互补能力整合到一个统一的主干中，用于广泛的计算病理学应用。该模型可在此https URL上获得。

英文摘要

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.

发表机构

• Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学生物医药与健康工程研究院，清华大学深圳国际研究生院）
• Department of Engineering Science, University of Oxford（牛津大学工程科学系）
• Department of Pathology, The First Affiliated Hospital of Sun Yat-sen University（中山大学附属第一医院病理科）
• Medical Optical Technology R&D Center, Research Institute of Tsinghua, Pearl River Delta（清华珠三角研究院医学光学技术研发中心）

机构由 AI 辅助整理，请以论文原文为准。