Domain-Adaptive Diagnosis of Lewy Body Disease with Transferability Aware Transformer.

Abstract

Lewy Body Disease (LBD) is a common but understudied dementia that poses a significant public health burden. It shares similar clinical signs with Alzheimer's disease (AD), with both conditions progressing through stages of normal cognition, mild cognitive impairment, and dementia. A major obstacle in LBD diagnosis is data scarcity, which limits the effectiveness of deep learning models. In contrast, AD datasets are more abundant, offering a potential avenue for knowledge transfer. However, LBD and AD data are typically collected from different sites using varied machines and protocols, resulting in a distinct domain shift. To effectively leverage AD data while mitigating domain shift, we propose a Transferability Aware Transformer (TAT) that adapts knowledge from AD to enhance LBD diagnosis. Our method utilizes structural connectivity (SC) derived from structural MRI as training data. Built on the attention mechanism, TAT assigns high weights to disease-transferable features while suppressing domain-specific ones, effectively reducing domain shift and improving diagnostic accuracy on limited LBD data. The experimental results demonstrate the effectiveness of TAT. Our work serves as the first to explore domain adaptation from AD to LBD study under data scarcity and domain shift scenarios, providing a promising framework for domain-adaptive diagnosis of rare diseases.