Robust Multimodal Representation Learning with Information Bottleneck and Balanced Fusion for Alzheimers Disease Classification

Given the capability of multimodal data to provide information from multiple perspectives, it is beneficial for improving the accuracy of Alzheimer’s disease (AD) classification. However, during practical multimodal learning, there is a phenomenon where certain modalities dominate the decision-making, leading to insufficient learning from other modalities. Moreover, redundant information within multimodal data can also hinder accurate classification decisions. Therefore, we propose a robust multimodal representation learning method for AD classification. Specifically, we first construct dedicated encoders for each multimodal data, including structural Magnetic Resonance Imaging (sMRI) images, Positron Emission Tomography (PET) images, and Mini-Mental State Examination (MMSE) scores, to extract their respective representations. Then, we employ the information bottleneck (IB) theory to guide the model to retain classification-related information in multimodal representations while reducing redundancy among modalities. Furthermore, to promote a balanced fusion of multimodal data, we redefine the classification confidence of each modality’s representation using an orthogonal weight classifier and then introduce a regularization term to amplify the prediction score differences for modalities with lower confidence. The experimental results on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset demonstrate that our method enhances the robustness of multimodal representations and achieves promising performance in AD-related classification tasks.