用660万张脑MRI切片训练出通用表示，无需标注也能跨任务精准分析。
BrainDINO: A Brain MRI Foundation Model for Generalizable Clinical Representation Learning
- 基于660万张未标注脑MRI切片，自监督训练统一表示模型。
- 在少样本条件下，跨10项临床任务表现优于现有基线模型。
- 适合医学影像研究者快速迁移应用，尤其数据稀缺场景下优势明显。
脑部MRI广泛应用于神经科学与临床领域，但多数学习方法为特定任务设计且依赖大量标注数据。本文提出BrainDINO——一种基于自蒸馏的自监督基础模型，在约660万张来自20个数据集的轴向切片上训练，涵盖人群、疾病及采集条件的广泛差异。使用冻结编码器搭配轻量任务头，该模型在肿瘤分割、神经退行性/发育性疾病分类、脑年龄估计、卒中后时间预测、分子状态预测、MRI序列分类和生存建模等任务中均实现有效迁移。在不同任务与监督范式下，其性能持续优于自然图像及专用于MRI的自监督基线，尤其在标签稀缺时优势显著。表征分析显示，无任务监督下特征仍具解剖结构组织性和病理敏感性。结果表明，大规模切片级自监督学习可生成支持多样化神经影像任务的统一表示，无需体积预训练或全网微调，为鲁棒、高效脑成像分析提供可扩展基础。代码已开源。
原文摘要 · Abstract (English)
Brain MRI underpins a wide range of neuroscientific and clinical applications, yet most learning-based methods remain task-specific and require substantial labeled data. Here we show that a single self-supervised representation can generalize across heterogeneous brain MRI endpoints. We trained BrainDINO, a self-distilled foundation model, on approximately 6.6 million unlabeled axial slices from 20 datasets encompassing broad variation in population, disease, and acquisition setting. Using a frozen encoder with lightweight task heads, BrainDINO supported transfer across tumor segmentation, neurodegenerative and neurodevelopmental conditions classification, brain age estimation, post-stroke temporal prediction, molecular status prediction, MRI sequence classification, and survival modeling. Across tasks and supervision regimes, BrainDINO consistently equaled or exceeded natural-image and MRI-specific self-supervised baselines, with particularly strong advantages under label scarcity. Representation analyses further showed anatomically organized and pathology-sensitive feature structure in the absence of task-specific supervision. Our findings indicate that large-scale slice-wise self-supervised learning can yield a unified brain MRI representation that supports diverse neuroimaging tasks without volumetric pretraining or full-network fine-tuning, establishing a scalable foundation for robust and data-efficient brain imaging analysis. Code is available at https://github.com/mclwu22/BrainDINO
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