TriFormer: A Multi-modal Transformer Framework For Mild Cognitive Impairment Conversion Prediction
This work addresses early prediction of Alzheimer's progression for patients with MCI, offering a potential tool for timely intervention, though it appears incremental in applying transformers to a specific medical domain.
The paper tackles predicting mild cognitive impairment (MCI) conversion to Alzheimer's disease by proposing TriFormer, a transformer-based framework that integrates multi-modal data from medical scans and clinical information, achieving state-of-the-art performance on ADNI datasets.
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-model 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 (ANDI)1 and ADNI2 datasets and outperforms previous state-of-the-art single and multi-modal methods.