Alapfogalmak
Deep learning-based multimodal fusion techniques have emerged as powerful tools for improving medical image classification by combining complementary information from various imaging modalities.
Kivonat
This paper provides a thorough review of the developments in deep learning-based multimodal fusion for medical classification tasks. The authors 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, the authors provide insight into the suitability of different network architectures for various multimodal fusion scenarios and application domains. Furthermore, they delve into challenges related to network architecture selection, handling incomplete multimodal data management, and the potential limitations of multimodal fusion. Finally, the authors spotlight the promising future of Transformer-based multimodal fusion techniques and give recommendations for future research in this rapidly evolving field.
Statisztikák
"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."
"The number of papers has increased yearly from 2016 to 2023, indicating that multimodal medical classification tasks based on deep learning have gained greater attention in recent years."
"Brain-related publications currently account for a substantial portion of multimodal studies."
Idézetek
"Deep learning-based multimodal fusion techniques have emerged as powerful tools for improving medical image classification by combining complementary information from various imaging modalities."
"Recognizing the potential of deep learning-based methods for multimodal medical image classification, researchers have increasingly focused on this area."
"To grant readers a more in-depth understanding of multimodal deep learning networks, we further segment intermediate fusion into single-level fusion, hierarchical fusion, and attention-based fusion."