IEEE Trans Med Imaging. 2023 Dec;42(12):3566-3578. doi: 10.1109/TMI.2023.3295489. Epub 2023 Nov 30.
Multi-modality medical data provide complementary information, and hence have been widely explored for computer-aided AD diagnosis. However, the research is hindered by the unavoidable missing-data problem, i.e., one data modality was not acquired on some subjects due to various reasons. Although the missing data can be imputed using generative models, the imputation process may introduce unrealistic information to the classification process, leading to poor performance. In this paper, we propose the Disentangle First, Then Distill (DFTD) framework for AD diagnosis using incomplete multi-modality medical images. First, we design a region-aware disentanglement module to disentangle each image into inter-modality relevant representation and intra-modality specific representation with emphasis on disease-related regions. To progressively integrate multi-modality knowledge, we then construct an imputation-induced distillation module, in which a lateral inter-modality transition unit is created to impute representation of the missing modality. The proposed DFTD framework has been evaluated against six existing methods on an ADNI dataset with 1248 subjects. The results show that our method has superior performance in both AD-CN classification and MCI-to-AD prediction tasks, substantially over-performing all competing methods.
多模态医学数据提供了互补信息，因此被广泛用于计算机辅助 AD 诊断。然而，研究受到不可避免的缺失数据问题的阻碍，即由于各种原因，某些数据模态未在某些受试者上获取。尽管可以使用生成模型对缺失数据进行插补，但插补过程可能会向分类过程引入不真实的信息，从而导致性能不佳。在本文中，我们提出了一种用于使用不完整多模态医学图像进行 AD 诊断的解缠优先，然后提取（DFTD）框架。首先，我们设计了一个区域感知解缠模块，将每个图像解缠为跨模态相关表示和模态内特定表示，重点是与疾病相关的区域。为了逐步整合多模态知识，我们构建了一个基于插补诱导的提取模块，其中创建了一个横向跨模态转换单元来插补缺失模态的表示。我们的方法已在具有 1248 个受试者的 ADNI 数据集上针对六个现有方法进行了评估。结果表明，我们的方法在 AD-CN 分类和 MCI 到 AD 预测任务中均具有优越的性能，明显优于所有竞争方法。
