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.

In recent years, the segmentation of anatomical or pathological structures using deep learning has experienced a widespread interest in medical image analysis. Remarkably successful performance has been reported in many imaging modalities and for a variety of clinical contexts to support clinicians in computer-assisted diagnosis, therapy or surgical planning purposes. However, despite the increasing amount of medical image segmentation challenges, there remains little consensus on which methodology perform best. Therefore, we examine in this paper the numerous developments and breakthroughs brought since the rise of U-Net inspired architectures. Especially, we focus on the technical challenges and emerging trends that the community is now focusing on, including conditional generative adversarial and cascaded networks, medical Transformers, contrastive learning, knowledge distillation, active learning, prior knowledge embedding, cross-modality learning, multi-structure analysis, federated learning or semi-supervised and self-supervised paradigms. We also suggest possible avenues to be further investigated in future research efforts.