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。

CMViM：用于 AD 分类的 3D 多模态表征学习的对比掩码 Vim 自编码器

CMViM: Contrastive Masked Vim Autoencoder for 3D Multi-modal  Representation Learning for AD classification

Alzheimer's Disease (AD) is a neurodegenerative condition characterized by
diverse progression rates among individuals, with changes in cortical thickness
(CTh) closely linked to its progression. Accurately forecasting CTh
trajectories can significantly enhance early diagnosis and intervention
strategies, providing timely care. However, the longitudinal data essential for
these studies often suffer from temporal sparsity and incompleteness,
presenting substantial challenges in modeling the disease's progression
accurately. Existing methods are limited, focusing primarily on datasets
without missing entries or requiring predefined assumptions about CTh
progression. To overcome these obstacles, we propose a conditional score-based
diffusion model specifically designed to generate CTh trajectories with the
given baseline information, such as age, sex, and initial diagnosis. Our
conditional diffusion model utilizes all available data during the training
phase to make predictions based solely on baseline information during inference
without needing prior history about CTh progression. The prediction accuracy of
the proposed CTh prediction pipeline using a conditional score-based model was
compared for sub-groups consisting of cognitively normal, mild cognitive
impairment, and AD subjects. The Bland-Altman analysis shows our
diffusion-based prediction model has a near-zero bias with narrow 95%
confidential interval compared to the ground-truth CTh in 6-36 months. In
addition, our conditional diffusion model has a stochastic generative nature,
therefore, we demonstrated an uncertainty analysis of patient-specific CTh
prediction through multiple realizations.

阿尔茨海默病是一种神经退行性情况，其进展速度因个体而异，与皮层厚度的变化密切相关，准确预测皮层厚度的轨迹可以显著提升早期诊断和干预策略，然而，长期数据经常遭受时间上的稀疏和不完整，我们提出了一种用于生成给定基线信息下皮层厚度轨迹的条件分数扩散模型，该模型在训练阶段利用所有可用数据，仅基于基线信息进行预测，无需先验的皮层厚度进展历史，通过对亚组进行比较，我们发现该模型预测的皮层厚度与真实值在 6-36 个月内几乎无偏差，有较窄的 95％置信区间，此外，我们的条件分数扩散模型具有随机生成的特性，因此我们对患者特定皮层厚度的预测进行了不确定性分析。