arXiv 2603.030302026-07-03cs.CV

BRIGHT：用于乳腺病理的协作式通用-专科基础模型

BRIGHT: A Collaborative Generalist-Specialist Foundation Model for Breast Pathology

Department of Breast Pathology and Laboratory, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin’s Clinical Research Center for Cancer, West Huanhu Road, Tianjin, China（天津医科大学肿瘤医院乳腺病理科及实验室，国家癌症临床研究中心，天津医科大学乳腺癌预防与治疗重点实验室，天津医科大学，教育部，天津癌症临床研究中心，西湖南路，天津，中国）；Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China（广东省人工智能在医学影像分析与应用重点实验室，广东省人民医院（广东省医学科学院），南方医科大学，广州，中国）

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,… 展开作者

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

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AI总结：

提出BRIGHT，首个针对乳腺病理的基础模型，采用通用-专科协作框架，在5.1万张全切片图像上训练，在25项内部验证任务中均达最优性能，优于5个通用模型。

摘要：

通用病理基础模型（PFMs）在大规模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，由于缺乏单个器官的大规模验证队列以及缺乏能够有效将广泛组织形态学知识转化为专科级解读所需器官特异性知识的定制训练范式，它们在特定器官系统内临床必需任务的完整谱系上的熟练程度仍是一个未解问题。在本研究中，我们提出了BRIGHT，这是首个专门为乳腺病理设计的PFM，在来自19家医院超过4万名患者队列的5.1万多张乳腺全切片图像上训练。BRIGHT采用协作式通用-专科框架来捕获通用和器官特异性特征。为了全面评估PFMs在乳腺肿瘤学上的性能，我们整理了迄今为止最大的多机构队列用于下游任务开发和评估，涵盖10家医院的2.5万多张WSI。验证队列覆盖了乳腺病理的完整谱系，包括25项不同的临床任务，涵盖诊断、生物标志物预测、治疗反应和生存预测。大量实验表明，BRIGHT优于五个领先的通用PFM，在25项内部验证任务中全部达到最先进（SOTA）性能，在11项外部验证任务中的4项达到SOTA，并具有出色的热图可解释性。通过在大规模验证队列上进行评估，本研究不仅展示了BRIGHT在乳腺肿瘤学中的临床实用性，还验证了协作式通用-专科范式，为开发特定器官系统的PFM提供了可扩展模板，加速了基础模型向临床应用的转化。

英文摘要：

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 ...

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