TriFormer: A Multi-modal Transformer Framework For Mild Cognitive Impairment Conversion Prediction

The prediction of mild cognitive impairment (MCI) conversion to Alzheimer's disease (AD) is important for early treatment to prevent or slow the progression of AD. To accurately predict the MCI conversion to stable MCI or progressive MCI, we propose TriFormer, a novel transformer-based framework with three specialized transformers to incorporate multi-modal data. TriFormer uses I) an image transformer to extract multi-view image features from medical scans, II) a clinical transformer to embed and correlate multi-modal clinical data, and III) a modality fusion transformer that produces an accurate prediction based on fusing the outputs from the image and clinical transformers. Triformer is evaluated on the Alzheimer's Disease Neuroimaging Initiative (ADNI) 1 and ADNI2 datasets and outperforms previous state-of-the-art single and multi-modal methods.

Automated summary

By utilizing the TriFormer framework, which incorporates multi-modal data, accurate prediction of MCI conversion to AD can be achieved. This is crucial for early treatment and prevention of AD progression.