TriFormer: A Multi-modal Transformer Framework For Mild Cognitive
  Impairment Conversion Prediction
Linfeng Liu Junyan Lyu Siyu Liu Xiaoying Tang Shekhar S. Chandra Fatima A. Nasrallah
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.