AI 中文总结
研究提出BrainNext通用自监督基础模型用于脑MRI分析，结合MAE预训练与三维双向xLSTM-UNet架构，从大量未标记数据学习，经微调用于下游任务，在FOMO 2025评估中表现出色，证明其可转移性和作为基础模型的潜力。
AI 中文摘要
使用自监督学习预训练的基础模型通过从大规模未标记数据中学习可转移表示，改变了计算机视觉。然而，现有的神经影像基础模型仍受限于特定任务训练、基于切片的学习策略或相对较小的预训练数据集，限制了其在不同脑MRI应用中的通用性。在这项工作中，我们提出了BrainNext，这是一种用于体积脑MRI分析的通用自监督基础模型。BrainNext将掩码自动编码器（MAE）预训练与原生三维双向xLSTM-UNet架构相结合，从跨越多种MRI模态的60,551个未标记脑MRI检查中学习丰富的解剖学表示。随后，通过轻量级的特定任务微调，将预训练模型应用于下游任务。我们在医学成像基础模型（FOMO）2025方法赛道上对BrainNext进行了评估，该赛道涵盖分类、分割和脑年龄估计，它在总体上获得了第二名，并在官方FOMO 2025挑战排行榜上的脑膜瘤分割任务中排名第一，证明了其在异构神经影像任务中的强大可转移性。这些结果凸显了大规模自监督预训练学习强大且可转移的体积表示的潜力，将BrainNext确立为用于各种脑MRI应用的可扩展基础模型。
英文摘要
Foundation models pretrained using self-supervised learning have transformed computer vision by learning transferable representations from large-scale unlabeled data. However, existing foundation models for neuroimaging remain limited by task-specific training, slice-based learning strategies, or relatively small pretraining datasets, restricting their generalizability across diverse brain MRI applications. In this work, we present BrainNext, a general-purpose self-supervised foundation model for volumetric brain MRI analysis. BrainNext combines masked autoencoder (MAE) pretraining with a native three-dimensional Bi-Directional xLSTM-UNet architecture to learn rich anatomical representations from 60,551 unlabeled brain MRI examinations spanning multiple MRI modalities. The pretrained model is subsequently adapted to downstream tasks through lightweight task-specific fine-tuning. We evaluate BrainNext on the Foundation Models for Medical Imaging (FOMO) 2025 Method Track, encompassing classification, segmentation, and brain-age estimation, where it achieved second place overall and ranked first in the meningioma segmentation task on the official FOMO 2025 challenge leaderboard, demonstrating strong transferability across heterogeneous neuroimaging tasks. These results highlight the potential of large-scale self-supervised pretraining to learn robust and transferable volumetric representations, establishing BrainNext as a scalable foundation model for diverse brain MRI applications.
