SLG-Net: Small-Large-Global Feature-Based Multilevel Feature Extraction Network for Ultrasound Image Segmentation
Automatic ultrasound image segmentation improves the efficiency of clinical diagnosis and decreases the workload of doctors. Many ultrasound image segmentation methods only focus on capturing local details and global dependencies, whereas ignoring large-scale context information. However, it is esse...
Saved in:
Main Authors: | Xinya Fan, Jianwen Hu, Kai Hu |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2025-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10836679/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Feature enhanced cascading attention network for lightweight image super-resolution
by: Feng Huang, et al.
Published: (2025-01-01) -
PZS‐Net: Incorporating of Frame Sequence and Multi‐Scale Priors for Prostate Zonal Segmentation in Transrectal Ultrasound
by: Jianguo Ju, et al.
Published: (2025-01-01) -
An Efficient Retinal Fluid Segmentation Network Based on Large Receptive Field Context Capture for Optical Coherence Tomography Images
by: Hang Qi, et al.
Published: (2025-01-01) -
Frequency and Texture Aware Multi-Domain Feature Fusion for Remote Sensing Scene Classification
by: Russo Ashraf, et al.
Published: (2025-01-01) -
FFUNet: A novel feature fusion makes strong decoder for medical image segmentation
by: Junsong Xie, et al.
Published: (2022-07-01)