基于3D脑部MRI构建通用自监督模型，提升医学影像诊断泛化能力。
Towards Generalisable Foundation Models for Brain MRI
- 用3D体积信息扩展DINO-v2，建模全脑解剖结构
- 在少标签和多对比度场景下优于现有方法
- 适合临床部署，减少对专家标注依赖
人工智能中的基础模型正推动医学影像发展，实现从大规模无标签数据中学习通用特征。本文提出BrainFound，一个基于DINO-v2的自监督脑部MRI基础模型，通过整合连续MRI切片的体积分量信息，将原为2D自然图像设计的视觉变换器扩展至3D脑部解剖建模，突破传统单切片范式。该模型支持单模态与多模态输入，可广泛应用于疾病检测、图像分割等下游任务，并在不同成像协议与临床场景中保持良好泛化性。实验表明，BrainFound在标签稀缺及多对比度设置下持续优于现有自监督预训练策略与监督基线。通过融合多种3D MRI模态（如T1、T2、FLAIR）信息，显著提升诊断准确率，降低对大量专家标注的依赖。其灵活性使其成为可扩展、实用的3D神经影像处理方案，具备临床应用与研究创新潜力。
原文摘要 · Abstract (English)
Foundation models in artificial intelligence (AI) are transforming medical imaging by enabling general-purpose feature learning from large-scale, unlabeled datasets. In this work, we introduce BrainFound, a self-supervised foundation model for brain MRI, built by extending DINO-v2, a vision transformer originally designed for 2D natural images. BrainFound adapts DINO-v2 to model full 3D brain anatomy by incorporating volumetric information from sequential MRI slices, moving beyond conventional single-slice paradigms. It supports both single- and multimodal inputs, enabling a broad range of downstream tasks, including disease detection and image segmentation, while generalising across varied imaging protocols and clinical scenarios. We show that BrainFound consistently outperforms existing self-supervised pretraining strategies and supervised baselines, particularly in label-scarce and multi-contrast settings. By integrating information from diverse 3D MRI modalities (e.g., T1, T2, FLAIR), it enhances diagnostic accuracy and reduces dependency on extensive expert annotations. This flexibility makes BrainFound a scalable and practical solution for 3D neuroimaging pipelines, with significant potential for clinical deployment and research innovation.
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