Multimodal medical imaging plays a pivotal role in clinical diagnosis and research, as it combines information from various imaging modalities to provide a more comprehensive understanding of the underlying pathology. Recently, deep learning-based multimodal fusion techniques have emerged as powerful tools for improving medical image classification. This review offers a thorough analysis of the developments in deep learning-based multimodal fusion for medical classification tasks. We explore the complementary relationships among prevalent clinical modalities and outline three main fusion schemes for multimodal classification networks: input fusion, intermediate fusion (encompassing single-level fusion, hierarchical fusion, and attention-based fusion), and output fusion. By evaluating the performance of these fusion techniques, we provide insight into the suitability of different network architectures for various multimodal fusion scenarios and application domains. Furthermore, we delve into challenges related to network architecture selection, handling incomplete multimodal data management, and the potential limitations of multimodal fusion. Finally, we spotlight the promising future of Transformer-based multimodal fusion techniques and give recommendations for future research in this rapidly evolving field.
@article{arxiv.2404.15022,
title = {A review of deep learning-based information fusion techniques for multimodal medical image classification},
author = {Yihao Li and Mostafa El Habib Daho and Pierre-Henri Conze and Rachid Zeghlache and Hugo Le Boité and Ramin Tadayoni and Béatrice Cochener and Mathieu Lamard and Gwenolé Quellec},
journal= {arXiv preprint arXiv:2404.15022},
year = {2024}
}