Advancements in Medical Information Fusion: A Review of Deep Learning Models and Their Applications
TL;DR
This review systematically explores deep learning‐driven advancements, focusing on their theoretical foundations, key technological breakthroughs and typical application scenarios, and systematically categorizes fusion architectures into data‐level, feature‐level, and decision‐level hierarchies across diverse modalities.
Abstract
Recent advancements in deep learning have transformed medical image processing, particularly in integrating multimodal data and optimizing complex tasks, positioning fusion models as the leading paradigm in contemporary research. Historically viewed as mere pixel‐level blending, this field has fundamentally evolved into a broader paradigm of Medical Information Fusion. This review systematically explores deep learning‐driven advancements, focusing on their theoretical foundations, key technological breakthroughs and typical application scenarios. First, we systematically categorize fusion architectures into data‐level, feature‐level, and decision‐level hierarchies across diverse modalities. Second, we address key technical challenges and methodological breakthroughs, focusing on advanced solutions for domain shift and semantic alignment, handling data scarcity via missing modality completion, and task‐specific optimization through multi‐task learning. Furthermore, we critically examine the clinical transitional potential of these models in key clinical scenarios, including advanced tumor diagnosis, smart surgery, personalized therapy, and brain function analysis. By synthesizing these insights, this work aims to provide a comprehensive understanding of the current landscape and future directions of medical information fusion, paving the way for advancements in precision medicine and improved healthcare outcomes.