LKDA-Net: Hierarchical transformer with large Kernel depthwise convolution attention for 3D medical image segmentation.
Since Transformers have demonstrated excellent performance in the segmentation of two-dimensional medical images, recent works have also introduced them into 3D medical segmentation tasks. For example, hierarchical transformers like Swin UNETR have reintroduced several prior knowledge of convolution...
Saved in:
| Main Authors: | Ming Li, Jingang Ma, Jing Zhao |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2025-01-01
|
| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0329806 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
DS-AdaptNet: An Efficient Retinal Vessel Segmentation Framework With Adaptive Enhancement and Depthwise Separable Convolutions
by: Shuting Chen, et al.
Published: (2025-01-01) -
Precision-Driven Semantic Segmentation of Pipe Gallery Diseases Using PipeU-NetX: A Depthwise Separable Convolution Approach
by: Wenbin Song, et al.
Published: (2025-06-01) -
Fault Diagnosis Method for Shearer Arm Gear Based on Improved S-Transform and Depthwise Separable Convolution
by: Haiyang Wu, et al.
Published: (2025-06-01) -
Weighted Feature Fusion Network Based on Large Kernel Convolution and Transformer for Multi-Modal Remote Sensing Image Segmentation
by: Jianxia Wang, et al.
Published: (2025-01-01) -
Real-Time Super Resolution Utilizing Dilation and Depthwise Separable Convolution
by: Che-Cheng Chang, et al.
Published: (2025-04-01)