Multi-modal Deep Learning

This article investigates deep learning methodologies for single-modality clinical data analysis, as a crucial precursor to multi-modal medical research. Building on Guo JingYuan's work, the study refines clinical data processing through Compact Convolutional Transformer (CCT), Patch Up, and the innovative CamCenterLoss technique, establishing a foundation for future multimodal investigations. The proposed methodology demonstrates improved prediction accuracy and at tentiveness to critically ill patients compared to Guo JingYuan's ResNet and StageNet approaches. Novelty that using image-pretrained vision transformer backbone to perform transfer learning time-series clinical data.