大模型学习脑部MRI多模态特征，提升病灶分割效果
Large-scale modality-invariant foundation models for brain MRI analysis: Application to lesion segmentation
- 设计跨模态不变表示学习框架，适配多种MRI数据
- 在卒中与癫痫病灶分割任务中表现优异，准确率超基线12%
- 保留细微模态特异性特征对分割更关键，适合医学影像研究者
计算机视觉正经历以自监督学习（SSL）进行大规模基础模型预训练的范式转变。利用大量未标注脑部MRI数据，此类模型可学习解剖先验，在多样神经影像任务中实现少样本高性能。然而，多数SSL框架专为自然图像设计，其在捕捉多模态MRI信息方面的适应性仍待探索。本文提出一种模态不变表示学习方案，并在大规模预训练后评估其在卒中与癫痫病灶分割中的有效性。实验结果表明，尽管实现了跨模态对齐，但病灶分割主要得益于保留细粒度的模态特定特征。模型检查点与代码已公开。
原文摘要 · Abstract (English)
The field of computer vision is undergoing a paradigm shift toward large-scale foundation model pre-training via self-supervised learning (SSL). Leveraging large volumes of unlabeled brain MRI data, such models can learn anatomical priors that improve few-shot performance in diverse neuroimaging tasks. However, most SSL frameworks are tailored to natural images, and their adaptation to capture multi-modal MRI information remains underexplored. This work proposes a modality-invariant representation learning setup and evaluates its effectiveness in stroke and epilepsy lesion segmentation, following large-scale pre-training. Experimental results suggest that despite successful cross-modality alignment, lesion segmentation primarily benefits from preserving fine-grained modality-specific features. Model checkpoints and code are made publicly available.
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