BRIGHT: A Collaborative Generalist-Specialist Foundation Model for Breast Pathology
作者: Xiaojing Guo, Jiatai Lin, Yumian Jia, Jingqi Huang, Zeyan Xu, Weidong Li, Longfei Wang, Jingjing Chen, Qin Li, Weiwei Wang, Lifang Cui, Wen Yue, Zhiqiang Cheng, Xiaolong Wei, Jianzhong Yu, Xia Jin, Baizhou Li, Honghong Shen, Jing Li, Chunlan Li, Yanfen Cui, Yi Dai, Yiling Yang, Xiaolong Qian, Liu Yang, Yang Yang, Guangshen Gao, Yaqing Li, Lili Zhai, Chenying Liu, Tianhua Zhang, Zhenwei Shi, Cheng Lu, Xingchen Zhou, Jing Xu, Miaoqing Zhao, Fang Mei, Jiaojiao Zhou, Ning Mao, Fangfang Liu, Chu Han, Zaiyi Liu
分类: cs.CV
发布日期: 2026-07-05
💡 一句话要点
提出BRIGHT以解决乳腺病理领域的特定任务挑战
🎯 匹配领域: 支柱九：具身大模型 (Embodied Foundation Models)
关键词: 乳腺病理 基础模型 通用-专科框架 临床应用 深度学习 图像分析 多机构验证
📋 核心要点
- 现有的通用病理基础模型在特定器官系统的临床任务上缺乏有效性，尤其是在乳腺病理领域。
- BRIGHT模型通过协作的通用-专科框架，结合广泛的组织形态学知识与器官特定的专业知识，解决了这一问题。
- 实验结果表明，BRIGHT在25个内部验证任务中均达到了最先进的性能，并在外部验证中也表现出色，显示出其临床应用潜力。
📝 摘要（中文）
通用病理基础模型（PFMs）在多器官数据集上预训练，已在多种临床应用中展现出卓越的预测能力。然而，针对特定器官系统的全方位临床任务的有效性仍然是一个未解的问题。本文提出BRIGHT，这是首个专为乳腺病理设计的PFM，基于来自19家医院的超过51,000张乳腺全切片图像进行训练。BRIGHT采用协作的通用-专科框架，捕捉普遍和器官特定特征。通过构建最大的多机构验证队列，BRIGHT在25个内部验证任务中实现了最先进的性能，并在11个外部验证任务中表现优异，验证了其在乳腺肿瘤学中的临床实用性。
🔬 方法详解
问题定义：本文旨在解决现有通用病理基础模型在乳腺病理特定任务上的有效性不足问题，尤其是缺乏针对单一器官的大规模验证队列。
核心思路：BRIGHT模型通过协作的通用-专科框架，结合通用特征与器官特定特征，旨在提升模型在乳腺病理领域的专业解读能力。
技术框架：BRIGHT的整体架构包括数据预处理、特征提取、模型训练和评估四个主要模块。首先，利用来自19家医院的乳腺全切片图像进行训练，随后进行特征提取和模型优化，最后在多机构验证队列上进行评估。
关键创新：BRIGHT的主要创新在于其协作的通用-专科框架，能够有效整合广泛的组织形态学知识与乳腺病理的专业知识，与传统的单一通用模型相比，显著提升了任务性能。
关键设计：在模型设计中，采用了特定的损失函数和网络结构，以确保模型在处理乳腺病理特定任务时的准确性和鲁棒性。
🖼️ 关键图片
📊 实验亮点
BRIGHT在25个内部验证任务中实现了最先进的性能，且在11个外部验证任务中也表现优异，显示出其在乳腺病理领域的强大能力。与五个领先的通用PFMs相比，BRIGHT在性能上有显著提升，尤其在热图可解释性方面表现出色。
🎯 应用场景
BRIGHT模型的潜在应用领域包括乳腺癌的早期诊断、预后评估和治疗反应预测等。其在乳腺病理学中的应用价值不仅提升了临床决策的准确性，也为个性化医疗提供了支持，未来可能推动更多专科领域的基础模型开发。
📄 摘要（原文）
Generalist pathology foundation models (PFMs), pretrained on large-scale multi-organ datasets, have demonstrated remarkable predictive capabilities across diverse clinical applications. However, their proficiency on the full spectrum of clinically essential tasks within a specific organ system remains an open question due to the lack of large-scale validation cohorts for a single organ as well as the absence of a tailored training paradigm that can effectively translate broad histomorphological knowledge into the organ-specific expertise required for specialist-level interpretation. In this study, we propose BRIGHT, the first PFM specifically designed for breast pathology, trained on over 51,000 breast whole-slide images derived from a cohort of over 40,000 patients across 19 hospitals. BRIGHT employs a collaborative generalist-specialist framework to capture both universal and organ-specific features. To comprehensively evaluate the performance of PFMs on breast oncology, we curate the largest multi-institutional cohorts to date for downstream task development and evaluation, comprising over 25,000 WSIs across 10 hospitals. The validation cohorts cover the full spectrum of breast pathology across 25 distinct clinical tasks spanning diagnosis, biomarker prediction, treatment response and survival prediction. Extensive experiments demonstrate that BRIGHT outperforms five leading generalist PFMs, achieving state-of-the-art (SOTA) performance in 25 of 25 internal validation tasks and in 4 of 11 external validation tasks with excellent heatmap interpretability. By evaluating on large-scale validation cohorts, this study not only demonstrates BRIGHT's clinical utility in breast oncology but also validates a collaborative generalist-specialist paradigm, providing a scalable template for developing PFMs on a specific organ system, accelerating the translation of foundation models into ...