SLGMA-UNet: Comprehensive Feature Aggregation With Context-Sensitive Attention for Medical Image Segmentation

SLGMA-UNet: Comprehensive Feature Aggregation With Context-Sensitive Attention for Medical Image Segmentation

Medical image segmentation is essential for clinical diagnosis and treatment planning. Existing segmentation methods encounter challenges such as managing size variations, interpreting contextual relationships, and integrating multi-source data. This paper introduces SLGMA-UNet, an enhanced architec...

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Bibliographic Details
Main Authors: Xinghuo Ye, Na Wang
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Attention mechanism
feature fusion
medical image segmentation
multi-scale
local-global information
UNet
Online Access:https://ieeexplore.ieee.org/document/11062523/
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https://ieeexplore.ieee.org/document/11062523/

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