Multi-modal Deep Learning

TL;DR

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.

Abstract

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

Figures (6)

• Figure 1: stage-net origin structure gao2020stagenet
• Figure 2: Baseline JingYuan's structure Guo2022
• Figure 3: The model architecture.
• Figure 4: Comparison of CCT to CVT hassani2021cct
• Figure 5: PatchUp process for two hidden representations associated with two samples faramarzi2020patchup