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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.03030 |
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| _version_ | 1866908862511054848 |
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| author | Guo, Xiaojing Lin, Jiatai Jia, Yumian Huang, Jingqi Xu, Zeyan Li, Weidong Wang, Longfei Chen, Jingjing Li, Qin Wang, Weiwei Cui, Lifang Yue, Wen Cheng, Zhiqiang Wei, Xiaolong Yu, Jianzhong Jin, Xia Li, Baizhou Shen, Honghong Li, Jing Li, Chunlan Cui, Yanfen Dai, Yi Yang, Yiling Qian, Xiaolong Yang, Liu Yang, Yang Gao, Guangshen Li, Yaqing Zhai, Lili Liu, Chenying Zhang, Tianhua Shi, Zhenwei Lu, Cheng Zhou, Xingchen Xu, Jing Zhao, Miaoqing Mei, Fang Zhou, Jiaojiao Mao, Ning Liu, Fangfang Han, Chu Liu, Zaiyi |
| author_facet | Guo, Xiaojing Lin, Jiatai Jia, Yumian Huang, Jingqi Xu, Zeyan Li, Weidong Wang, Longfei Chen, Jingjing Li, Qin Wang, Weiwei Cui, Lifang Yue, Wen Cheng, Zhiqiang Wei, Xiaolong Yu, Jianzhong Jin, Xia Li, Baizhou Shen, Honghong Li, Jing Li, Chunlan Cui, Yanfen Dai, Yi Yang, Yiling Qian, Xiaolong Yang, Liu Yang, Yang Gao, Guangshen Li, Yaqing Zhai, Lili Liu, Chenying Zhang, Tianhua Shi, Zhenwei Lu, Cheng Zhou, Xingchen Xu, Jing Zhao, Miaoqing Mei, Fang Zhou, Jiaojiao Mao, Ning Liu, Fangfang Han, Chu Liu, Zaiyi |
| contents | 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 approximately 210 million histopathology tiles from 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 24 distinct clinical tasks spanning diagnosis, biomarker prediction, treatment response and survival prediction. Extensive experiments demonstrate that BRIGHT outperforms three leading generalist PFMs, achieving state-of-the-art (SOTA) performance in 21 of 24 internal validation tasks and in 5 of 10 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_03030 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | BRIGHT: A Collaborative Generalist-Specialist Foundation Model for Breast Pathology Guo, Xiaojing Lin, Jiatai Jia, Yumian Huang, Jingqi Xu, Zeyan Li, Weidong Wang, Longfei Chen, Jingjing Li, Qin Wang, Weiwei Cui, Lifang Yue, Wen Cheng, Zhiqiang Wei, Xiaolong Yu, Jianzhong Jin, Xia Li, Baizhou Shen, Honghong Li, Jing Li, Chunlan Cui, Yanfen Dai, Yi Yang, Yiling Qian, Xiaolong Yang, Liu Yang, Yang Gao, Guangshen Li, Yaqing Zhai, Lili Liu, Chenying Zhang, Tianhua Shi, Zhenwei Lu, Cheng Zhou, Xingchen Xu, Jing Zhao, Miaoqing Mei, Fang Zhou, Jiaojiao Mao, Ning Liu, Fangfang Han, Chu Liu, Zaiyi Computer Vision and Pattern Recognition 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 approximately 210 million histopathology tiles from 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 24 distinct clinical tasks spanning diagnosis, biomarker prediction, treatment response and survival prediction. Extensive experiments demonstrate that BRIGHT outperforms three leading generalist PFMs, achieving state-of-the-art (SOTA) performance in 21 of 24 internal validation tasks and in 5 of 10 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. |
| title | BRIGHT: A Collaborative Generalist-Specialist Foundation Model for Breast Pathology |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.03030 |