DeepAI AI Chat
Log In Sign Up

DXM-TransFuse U-net: Dual Cross-Modal Transformer Fusion U-net for Automated Nerve Identification

by   Baijun Xie, et al.

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.


page 3

page 6

page 7


Husformer: A Multi-Modal Transformer for Multi-Modal Human State Recognition

Human state recognition is a critical topic with pervasive and important...

On Uni-Modal Feature Learning in Supervised Multi-Modal Learning

We abstract the features (i.e. learned representations) of multi-modal d...

Hierarchical Cross-modal Transformer for RGB-D Salient Object Detection

Most of existing RGB-D salient object detection (SOD) methods follow the...

AXM-Net: Cross-Modal Context Sharing Attention Network for Person Re-ID

Cross-modal person re-identification (Re-ID) is critical for modern vide...

COEM: Cross-Modal Embedding for MetaCell Identification

Metacells are disjoint and homogeneous groups of single-cell profiles, r...

Toward Clinically Assisted Colorectal Polyp Recognition via Structured Cross-modal Representation Consistency

The colorectal polyps classification is a critical clinical examination....

Dyadformer: A Multi-modal Transformer for Long-Range Modeling of Dyadic Interactions

Personality computing has become an emerging topic in computer vision, d...