IT: An interpretable transformer model for Alzheimer's disease prediction based on PET/MR images

Abstract

Alzheimer's disease (AD) represents a significant challenge due to its progressive neurodegenerative impact, particularly within an aging global demographic. This underscores the critical need for developing sophisticated diagnostic tools for its early detection and precise monitoring. Within this realm, PET/MR imaging stands out as a potent dual-modality approach that transforms sensor data into detailed perceptual mappings, thereby enriching our grasp of brain pathophysiology. To capitalize on the strengths of PET/MR imaging in diagnosing AD, we have introduced a novel deep learning framework named "IT", which is inspired by the Transformer architecture. This innovative model adeptly captures both local and global characteristics within the imaging data, refining these features through advanced feature engineering techniques to achieve a synergistic integration. The efficiency of our model is underscored by robust experimental validation, wherein it delivers superior performance on a host of evaluative benchmarks, all while maintaining low demands on computational resources. Furthermore, the features we extracted resonate with established medical theories regarding feature distribution and usage efficiency, enhancing the clinical relevance of our findings. These insights significantly bolster the arsenal of tools available for AD diagnostics and contribute to the broader narrative of deciphering brain functionality through state-of-the-art imaging modalities.