Segmentation of Lung Lesions through Bilateral Learning Branches to Aggregating Contextual and Local Characteristics
Abstract Detecting and analyzing lung lesion regions using artificial intelligence is of great significance in the medical diagnosis of lung CT images, which can substantially improve the efficiency of doctors. However, segmentation of the inflammatory region in the CT image of the lung remains chal...
Main Authors: | Hao Niu, Linjing Li, Bo Yuan, Min Zhu, Xiuyuan Xu, Xi Lu, Fengming Luo, Zhang Yi |
---|---|
Format: | Article |
Language: | English |
Published: |
Springer
2024-02-01
|
Series: | International Journal of Computational Intelligence Systems |
Subjects: | |
Online Access: | https://doi.org/10.1007/s44196-023-00401-8 |
Similar Items
-
3D Segmentation of Necrotic Lung Lesions in CT Images Using Self-Supervised Contrastive Learning
by: Yiqiao Liu, et al.
Published: (2024-01-01) -
Histological spectrum, bilaterality, and clinical evaluation of ovarian lesions – A 10-year study in a rural tertiary hospital of India
by: Santosh Kumar Mondal, et al.
Published: (2020-01-01) -
Cascaded Contextual Reasoning for Large-Scale Point Cloud Semantic Segmentation
by: Fengyi Zhang, et al.
Published: (2023-01-01) -
A Lightweight Residual Model for Corrosion Segmentation with Local Contextual Information
by: Jingxu Huang, et al.
Published: (2022-09-01) -
CoT-UNet++: A medical image segmentation method based on contextual transformer and dense connection
by: Yijun Yin, et al.
Published: (2023-03-01)