基于600万张MRI图像训练的通用模型，助力低数据场景下AI诊断开发。
MRI-CORE: A Foundation Model for Magnetic Resonance Imaging
- 用超百万张MRI切片训练通用模型，覆盖18个身体部位。
- 在13个数据稀缺任务中超越现有方法，零样本分割表现优异。
- 适合医疗AI研究者快速构建低数据依赖的模型，尤其关注隐私保护。
磁共振成像（MRI）与深度学习结合，在自动化诊断与预后工具方面前景广阔。然而，新模型训练需大量标注数据，受限于精准标注成本高和数据隐私问题。为此，我们提出MRI-CORE，一个在超过11万个MRI体积、18个体部位置、总计逾600万张图像切片上训练的视觉基础模型。实验表明，该模型在13个数据受限的分割任务、图像分类及零样本分割中均显著优于当前最优方法，展现出强大的数据高效建模潜力。我们还分析了不同预训练策略对模型性能的影响，并首次揭示预训练数据与下游任务数据相似性与迁移性能之间的关系。模型已开源，采用宽松许可协议。
原文摘要 · Abstract (English)
The widespread use of Magnetic Resonance Imaging (MRI) in combination with deep learning shows promise for many high-impact automated diagnostic and prognostic tools. However, training new models requires large amounts of labeled data, a challenge due to high cost of precise annotations and data privacy. To address this issue, we introduce the MRI-CORE, a vision foundation model trained using more than 6 million slices from over 110 thousand MRI volumes across 18 body locations. Our experiments show notable improvements in performance over state-of-the-art methods in 13 data-restricted segmentation tasks, as well as in image classification, and zero-shot segmentation, showing the strong potential of MRI-CORE to enable data-efficient development of artificial intelligence models. We also present data on which strategies yield most useful foundation models and a novel analysis relating similarity between pre-training and downstream task data with transfer learning performance. Our model is publicly available with a permissive license.
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