Developing Foundation Models for medical image analysis is essential to overcome the unique challenges of radiological tasks. The first challenges of this kind for 3D brain MRI, SSL3D and FOMO25, were held at MICCAI 2025. Our solution ranked first in tracks of both contests. It relies on a U-Net CNN architecture combined with strategies leveraging anatomical priors and neuroimaging domain knowledge. Notably, our models trained 1-2 orders of magnitude faster and were 10 times smaller than competing transformer-based approaches. Models are available here: https://github.com/jbanusco/BrainFM4Challenges.

翻译：开发用于医学图像分析的基础模型对于克服放射学任务中的独特挑战至关重要。首届针对3D脑部MRI的此类挑战赛SSL3D与FOMO25于MICCAI 2025举办。我们的解决方案在两项赛事的多项赛道中均位列第一。该方法基于U-Net CNN架构，并结合了利用解剖学先验知识与神经影像领域知识的策略。值得注意的是，我们的模型训练速度比基于Transformer的竞争方法快1-2个数量级，且模型体积缩小10倍。模型已开源：https://github.com/jbanusco/BrainFM4Challenges。