Multi-modal Deep Learning
Chen Yuhua
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.The study highlights the potential of CCT, Patch Up, and novel
CamCenterLoss in processing single modality clinical data within deep learning
frameworks, paving the way for future multimodal medical research and promoting
precision and personalized healthcare