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

2026-03-03

AI Summary

This work addresses the limited performance of existing general-purpose foundation models in comprehensive clinical tasks within a single organ system—such as the breast—due to insufficient large-scale validation and organ-specific training. To overcome this, we propose BRIGHT, the first breast-specialized foundation model, trained on 210 million tissue patches and 51,000 whole-slide images of breast specimens. BRIGHT employs a generalist–specialist collaborative training framework that integrates both universal and organ-specific pathological features, enabling support for 24 diverse tasks spanning diagnosis, biomarker prediction, treatment response assessment, and survival analysis. The model achieves state-of-the-art performance across 21 internal and 5 external evaluation tasks, significantly outperforming general-purpose counterparts, and establishes the largest multicenter validation cohort to date, offering a scalable paradigm for organ-specific pathology foundation models.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Computer Vision: Medical and Biological Imaging
• Cognitive Modeling & Cognitive Systems: Other Foundations of Cognitive Modeling & Systems

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

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.

Problem

• pathology foundation model
• breast pathology
• organ-specific expertise
• clinical validation
• generalist-specialist paradigm

Innovation

• collaborative generalist-specialist framework
• breast pathology foundation model
• large-scale multi-institutional validation
• organ-specific feature learning
• histopathology foundation model