Cross-Modal Contrastive Learning for Enhanced Medical Multimodal Diagnosis

Abstract

The increasing availability of heterogeneous medical data, including imaging, electronic health records, and genomic information, offers unprecedented opportunities for accurate and early disease diagnosis. However, effectively integrating these multimodal data sources remains a significant challenge due to differences in data formats, distributions, and information content. In this study, we propose a cross-modal contrastive learning framework designed to enhance multimodal medical diagnosis by learning aligned and discriminative representations across heterogeneous data modalities. The framework employs modality-specific feature extraction networks and a cross-modal contrastive loss to maximize agreement between semantically related data while minimizing correlations between unrelated pairs. Extensive experiments on benchmark multimodal medical datasets demonstrate that our approach outperforms conventional multimodal fusion methods in terms of diagnostic accuracy, area under the curve (AUC), and F1-score. Ablation studies highlight the importance of each modality and the contrastive loss formulation in achieving robust representation learning. Visualization of learned embeddings further confirms effective cross-modal alignment. The proposed framework provides a generalizable and interpretable approach for integrating diverse medical data, offering significant potential for improving clinical decision-making and patient outcomes. Future work will explore longitudinal data integration, additional medical modalities, and explainable mechanisms for enhanced transparency in clinical applications.