GLIMS: Attention-guided lightweight multi-scale hybrid network for volumetric semantic segmentation

Computer Science

• Hybrid Network 100%
• Image Segmentation 100%
• Segmentation Task 100%
• Range Dependency 100%
• Convolutional Neural Network 50%
• Network Segmentation 50%
• Segmentation Approach 50%
• local feature 50%
• Data Availability 50%
• Global Feature 50%
• Self-Attention Mechanism 50%
• Swin Transformer 50%
• Spatial Correlation 50%
• Outstanding Performance 50%

Keyphrases

• GLIMS 100%
• Multiscale Hybrid Network 100%
• Attention-guided 100%
• Volumetric Semantic Segmentation 100%
• Training Parameters 37%
• Segmentation Problem 25%
• Long-range Dependence 25%
• Swin UNETR 25%
• Publicly Available 12%
• High Performance 12%
• Spatial Correlation 12%
• Excellent Performance 12%
• Convolutional Neural Network 12%
• Aggregator 12%
• Segmentation Approach 12%
• Volumetric Segmentation 12%
• Data Availability 12%
• Glioblastoma 12%
• Local Features 12%
• Transformer-based 12%
• Medical Image Segmentation 12%
• Feature Correlation 12%
• Multi-organ 12%
• Global Features 12%
• Self-attention Mechanism 12%
• Low-level Features 12%
• Channel-wise Attention 12%
• Convolutional Architecture 12%
• Swin Transformer 12%
• Spatial Attention 12%
• Efficient Attention 12%
• CT Segmentation 12%
• Attention Block 12%
• Transformer Architecture 12%
• Guided Segmentation 12%
• Inductive Bias 12%
• Segmentation Network 12%
• Convolutional Block 12%

Engineering

• Limitations 100%
• Multiscale 100%
• Spatial Correlation 100%
• Aggregator 100%
• Level Feature 100%
• Medical Image Segmentation 100%
• Convolutional Neural Network 100%