先降维，再编码：面向脑部MRI中2D基础模型的多尺度体数据降维方法

中文摘要 AI 辅助

预训练的2D基础模型为脑部结构磁共振成像（sMRI）提供了专用3D预训练的一种实用替代方案，但将其用于体数据时，需要弥合2D编码器与3D体积输入之间的不匹配。现有方法通常独立编码切片，随后再整合其特征。我们提出了多尺度体数据降维（MVR），一种先降维后编码的方法，在基础模型编码之前，将每个解剖视图从D个切片压缩为M（M << D）个互补的2D分量。MVR结合了从原始穿平面强度中导出的非中心化PCA基础分量，以及由多尺度空间描述符构建的残差细节分量。该降维过程基于训练体积进行估计，无需诊断标签或基于梯度的优化，且此后保持固定。所得分量由共享的冻结2D基础模型独立处理，并拼接用于线性探测。在此冻结编码器设置下，与所评估的2D转3D自适应方法及简单输入降维基线相比，MVR在ADNI、OASIS和ABIDE上取得了强劲的整体性能，同时也能从ADNI很好地泛化到AIBL。

英文摘要

Pretrained 2D foundation models offer a practical alternative to dedicated 3D pretraining for brain structural magnetic resonance imaging (sMRI), but their use on volumetric data requires bridging the mismatch between a 2D encoder and a 3D volume input. Existing methods typically encode slices independently and integrate their features afterwards. We introduce Multiscale Volumetric Reduction (MVR), a reduce-then-encode approach that compresses each anatomical view from (D) slices into (M << D) complementary 2D components before foundation-model encoding. MVR combines an uncentered-PCA base component derived from the original through-plane intensities with residual detail components constructed from multiscale spatial descriptors. The reduction is estimated from the training volumes without diagnostic labels or gradient-based optimization and remains fixed thereafter. The resulting components are independently processed by a shared frozen 2D foundation model and concatenated for linear probing. Under this frozen-encoder setting, MVR achieves strong overall performance across ADNI, OASIS, and ABIDE relative to the evaluated 2D-to-3D adaptation methods and simple input-reduction baselines, while also generalizing strongly from ADNI to AIBL.

发表机构

• Macquarie University（麦考瑞大学）
• Australian National University（澳大利亚国立大学）
• University of Sydney（悉尼大学）

机构由 AI 辅助整理，请以论文原文为准。