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

arXiv: https://arxiv.org/abs/2507.08839 · HTML: https://mlsanity.com/paper/2507.08839/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 24 references that are in the map.
- First posted on arXiv 7 Jul 2025.

## Builds on (most cited first, 12 of 12)

- [TVT: Transferable Vision Transformer for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2108.05988/) (2021)
- [Safe Self-Refinement for Transformer-based Domain Adaptation](https://mlsanity.com/paper/2204.07683/) (2022)
- [Knowledge Distillation and Dataset Distillation of Large Language Models: Emerging Trends, Challenges, and Future Directions](https://mlsanity.com/paper/2504.14772/) (2025)
- [GP-GPT: Large Language Model for Gene-Phenotype Mapping](https://mlsanity.com/paper/2409.09825/) (2024)
- [Core-Periphery Principle Guided Redesign of Self-Attention in Transformers](https://mlsanity.com/paper/2303.15569/) (2023)
- [Brain-Adapter: Enhancing Neurological Disorder Analysis with Adapter-Tuning Multimodal Large Language Models](https://mlsanity.com/paper/2501.16282/) (2025)
- [Feature Fusion Transferability Aware Transformer for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2411.07794/) (2024)
- [Classification of Mild Cognitive Impairment Based on Dynamic Functional Connectivity Using Spatio-Temporal Transformer](https://mlsanity.com/paper/2501.16409/) (2025)
- [Core-Periphery Principle Guided State Space Model for Functional Connectome Classification](https://mlsanity.com/paper/2503.14655/) (2025)
- [BrainNet-MoE: Brain-Inspired Mixture-of-Experts Learning for Neurological Disease Identification](https://mlsanity.com/paper/2503.07640/) (2025)
- [Robust Core-Periphery Constrained Transformer for Domain Adaptation](https://mlsanity.com/paper/2308.13515/) (2023)
- [Representative Functional Connectivity Learning for Multiple Clinical groups in Alzheimer's Disease](https://mlsanity.com/paper/2106.07622/) (2021)

## Built on by (most cited first, 1 of 1)

- [DCMM-Transformer: Degree-Corrected Mixed-Membership Attention for Medical Imaging](https://mlsanity.com/paper/2511.12047/) (2025)

## Abstract (arXiv)

Lewy Body Disease (LBD) is a common yet understudied form of dementia that imposes a significant burden on public health. It shares clinical similarities with Alzheimer's disease (AD), as both progress 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. In contrast, AD datasets are more abundant, offering potential for knowledge transfer. However, LBD and AD data are typically collected from different sites using different 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 adaptively assigns greater weights to disease-transferable features while suppressing domain-specific ones, thereby reducing domain shift and improving diagnostic accuracy with limited LBD data. The experimental results demonstrate the effectiveness of TAT. To the best of our knowledge, this is the first study to explore domain adaptation from AD to LBD under conditions of data scarcity and domain shift, providing a promising framework for domain-adaptive diagnosis of rare diseases.