Multi-Modal Deep Learning for Medical Imaging: From Segmentation to Clinical Decision Support

Abstract

Multi-modal deep learning has emerged as an effective strategy for combining heterogeneous medical imaging signals to support clinical decision processes. Advances in imaging technologies and data fusion enable richer diagnostic evidence, which enhances segmentation accuracy and predictive perfor- mance. This article presents a comprehensive analysis of multi- modal architectures, their integration patterns, and their role in clinical decision support. A unified methodology is introduced for fusing spatial, temporal, and spectral features. Experimental evaluations illustrate the performance of the proposed multi- modal pipeline across representative imaging tasks. Visualization, tables, and charts depict the behavior of the underlying models in a clinically relevant setting.