论文标题
DXM-TransFuse U-NET：用于自动化神经识别的双跨模式变压器融合U-NET
DXM-TransFuse U-net: Dual Cross-Modal Transformer Fusion U-net for Automated Nerve Identification
论文作者
论文摘要
在外科手术过程中，准确的神经识别至关重要，以防止对神经组织的任何损害。神经损伤会导致患者和金融衰退的长期有害影响。在这项研究中，我们使用U-NET架构开发了一个深入学习的网络框架，该体系结构在瓶颈上具有基于变压器块的融合模块，以识别多模式光学成像系统的神经组织。通过独立利用和提取每种模式的特征图，并使用每个模态信息进行跨模式相互作用，我们旨在提供一种解决方案，以进一步提高成像系统的有效性，以实现非侵入性术中神经识别。
Accurate nerve identification is critical during surgical procedures for preventing any damages to nerve tissues. Nerve injuries can lead to long-term detrimental effects for patients as well as financial overburdens. In this study, we develop a deep-learning network framework using the U-Net architecture with a Transformer block based fusion module at the bottleneck to identify nerve tissues from a multi-modal optical imaging system. By leveraging and extracting the feature maps of each modality independently and using each modalities information for cross-modal interactions, we aim to provide a solution that would further increase the effectiveness of the imaging systems for enabling the noninvasive intraoperative nerve identification.
