基于深度学习的多模态医学图像分类信息融合技术综述
A review of deep learning-based information fusion techniques for multimodal medical image classification
AI总结：
本文综述了基于深度学习的多模态医学图像分类融合技术，详细分析了输入、中间和输出三种融合方案及其网络架构适用性，并探讨了当前挑战与基于Transformer的未来研究方向。
摘要：
多模态医学成像在临床诊断和研究中发挥着关键作用，因为它结合了来自各种成像模态的信息，从而对潜在的病理学提供更全面的理解。近年来，基于深度学习的多模态融合技术已成为改善医学图像分类的有力工具。本综述对深度学习在医学分类任务中的多模态融合发展进行了全面分析。我们探讨了常见临床模态之间的互补关系，并概述了多模态分类网络的三种主要融合方案：输入融合、中间融合（包括单层融合、分层融合和基于注意力的融合）以及输出融合。通过评估这些融合技术的性能，我们深入了解了不同网络架构对各种多模态融合场景和应用领域的适用性。此外，我们深入探讨了与网络架构选择、处理不完整多模态数据管理以及多模态融合的潜在局限性相关的挑战。最后，我们重点关注了基于Transformer的多模态融合技术的广阔前景，并为这一快速发展领域的未来研究提出了建议。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
