Multimodal Neuroimaging Feature Learning for Multiclass Diagnosis of Alzheimer's Disease

TLDR

Accurate diagnosis of Alzheimer’s disease is essential for patient care and will become increasingly important as disease‑modifying agents become available, yet current machine learning methods are limited by inefficient neuroimaging biomarker representation. The study proposes a deep‑learning diagnostic framework to aid Alzheimer’s disease diagnosis. The framework employs a zero‑masking strategy for multimodal data fusion to extract complementary information from multiple neuroimaging modalities. The method outperforms previous state‑of‑the‑art workflows, achieving gains in both binary and multiclass AD classification while potentially requiring less labeled data, though its advantages and limitations are discussed.

Abstract

The accurate diagnosis of Alzheimer's disease (AD) is essential for patient care and will be increasingly important as disease modifying agents become available, early in the course of the disease. Although studies have applied machine learning methods for the computer-aided diagnosis of AD, a bottleneck in the diagnostic performance was shown in previous methods, due to the lacking of efficient strategies for representing neuroimaging biomarkers. In this study, we designed a novel diagnostic framework with deep learning architecture to aid the diagnosis of AD. This framework uses a zero-masking strategy for data fusion to extract complementary information from multiple data modalities. Compared to the previous state-of-the-art workflows, our method is capable of fusing multimodal neuroimaging features in one setting and has the potential to require less labeled data. A performance gain was achieved in both binary classification and multiclass classification of AD. The advantages and limitations of the proposed framework are discussed.