Multimodal Learning in Medical Image Analysis

Abstract

Medical image analysis is progressing from a unimodal visual assessment phase toward data-driven, multimodal learning methods that reflect clinical reasoning processes more closely. Traditional deep learning approaches primarily rely on imaging data; additional information from electronic health records, genetics, or other physiological signals are disregarded in terms of complementing the analysis of imaging data. This chapter reviews existential and conceptual work for multimodal learning in medical image analysis, discussing multimodal methods, approaches to multimodal integration, and challenges for multimodal learning. We will discuss early, late, and hybrid fusion frameworks that combine heterogeneous sources of data in advanced representation techniques (e.g., contrastive learning, joint embeddings, and cross-attention). We examine for current state-of-the-art multimodal architectures - Vision Transformers, Graph Neural Networks, and large vision-language models, such as MedCLIP. The goal is to describe the ways it&s;d seek to align visual and textual representations for diagnostic and prognostic uses. Application examples are provided in radiology, pathology, and surgery. Novel multimodal approaches look to improve the precision of the diagnostic pathway and improve interpretability, trust, and personalization when applying the findings of the diagnostic pathway.