CARE：一种具有自适应区域建模的分子引导基础模型用于全切片图像分析
CARE: A Molecular-Guided Foundation Model with Adaptive Region Modeling for Whole Slide Image Analysis
AI总结：
CARE通过自适应区域建模和分子引导，提升全切片图像分析的性能，实现对病理区域的精准识别与分类，优于现有基础模型。
摘要：
基础模型在计算病理学中取得了显著成功，但在病理区域的异质性和非均匀组织结构方面存在不足。为此，本文提出CARE，一种病理基础模型，通过两阶段预训练策略：首先通过34,277张全切片图像进行自监督单模预训练，学习形态表示；其次利用RNA和蛋白质谱进行跨模态对齐，优化自适应区域的构建和表示。CARE能够识别生物相关模式，生成不规则但连贯的组织区域，并选择最具代表性的区域作为ROI。CARE支持多种病理任务，使用ROI特征或聚合自适应区域的滑片级特征。仅用常规基础模型1/10的预训练数据，CARE在33个下游基准测试中均表现优异，包括形态分类、分子预测和生存分析，并在整体上优于其他基础模型基线。
英文摘要：
Foundation models have recently achieved impressive success in computational pathology, demonstrating strong generalization across diverse histopathology tasks. However, existing models overlook the heterogeneous and non-uniform organization of pathological regions of interest (ROIs) because they rely on natural image backbones not tailored for tissue morphology. Consequently, they often fail to capture the coherent tissue architecture beyond isolated patches, limiting interpretability and clinical relevance. To address these challenges, we present Cross-modal Adaptive Region Encoder (CARE), a foundation model for pathology that automatically partitions WSIs into several morphologically relevant regions. Specifically, CARE employs a two-stage pretraining strategy: (1) a self-supervised unimodal pretraining stage that learns morphological representations from 34,277 whole-slide images (WSIs) without segmentation annotations, and (2) a cross-modal alignment stage that leverages RNA and protein profiles to refine the construction and representation of adaptive regions. This molecular guidance enables CARE to identify biologically relevant patterns and generate irregular yet coherent tissue regions, selecting the most representative area as ROI. CARE supports a broad range of pathology-related tasks, using either the ROI feature or the slide-level feature obtained by aggregating adaptive regions. Based on only one-tenth of the pretraining data typically used by mainstream foundation models, CARE achieves superior average performance across 33 downstream benchmarks, including morphological classification, molecular prediction, and survival analysis, and outperforms other foundation model baselines overall.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
