Deep Learning Architectures For Multimodal Medical Data Integration

Abstract

Multimodal data integration is gaining traction in medical image analysis, enabling the use of diverse data sources to improve downstream tasks. Deep Learning approaches have proliferated, employing generic architectures and a data-driven paradigm. While initial efforts have yielded positive results, they lack inherent adaptation to the peculiarities of medical multimodality. Bridging representations across signal pairs and aligning disparate modalities provide more robust performance. Specifically, representation learning, explicitly learning transferable feature extraction models, has emerged as an important research avenue. Contrastive learning and visual-language pre-training provide methods to learn joint embedding spaces. The proposed multimodal evaluation setup examines several public datasets, offering a well-designed statistical analysis framework and research-practice reproducibility. Baseline models explore early-and late-fusion scenarios for multimodal emotion recognition and sickness prediction from facial expression. Initial results indicate representative power and proper data alignment as crucial elements. As multimodality gains momentum in Deep Learning research, bridging modalities and demonstrating clear real-world applications pave the way for impactful contributions.