Alzheimer's disease (AD) is an incurable neurodegenerative condition leading
to cognitive and functional deterioration. Given the lack of a cure, prompt and
precise AD diagnosis is vital, a complex process dependent on multiple factors
and multi-modal data. While successful efforts have been made to integrate
multi-modal representation learning into medical datasets, scant attention has
been given to 3D medical images. In this paper, we propose Contrastive Masked
Vim Autoencoder (CMViM), the first efficient representation learning method
tailored for 3D multi-modal data. Our proposed framework is built on a masked
Vim autoencoder to learn a unified multi-modal representation and
long-dependencies contained in 3D medical images. We also introduce an
intra-modal contrastive learning module to enhance the capability of the
multi-modal Vim encoder for modeling the discriminative features in the same
modality, and an inter-modal contrastive learning module to alleviate
misaligned representation among modalities. Our framework consists of two main
steps: 1) incorporate the Vision Mamba (Vim) into the mask autoencoder to
reconstruct 3D masked multi-modal data efficiently. 2) align the multi-modal
representations with contrastive learning mechanisms from both intra-modal and
inter-modal aspects. Our framework is pre-trained and validated ADNI2 dataset
and validated on the downstream task for AD classification. The proposed CMViM
yields 2.7\% AUC performance improvement compared with other state-of-the-art
methods.

我们提出了一种针对 3D 多模态数据的首个高效表示学习方法 Contrastive Masked Vim Autoencoder（CMViM），该方法利用传统的 Vision Mamba（Vim）自动编码器结构实现对三维医学图像的统一多模态表示，通过内部对比度学习模块和跨模态对比度学习模块来增强其判别特征建模能力，并在 AD 分类任务中验证，与其他最先进方法相比，提高了 2.7％的 AUC 性能。