FEBE-Net: Feature Exploration Attention and Boundary Enhancement Refinement Transformer Network for Bladder Tumor Segmentation

FEBE-Net: Feature Exploration Attention and Boundary Enhancement Refinement Transformer Network for Bladder Tumor Segmentation

The automatic and accurate segmentation of bladder tumors is a key step in assisting urologists in diagnosis and analysis. At present, existing Transformer-based methods have limited ability to restore local detail features and insufficient boundary segmentation capabilities. We propose FEBE-Net, wh...

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Bibliographic Details
Main Authors: Chao Nie, Chao Xu, Zhengping Li
Format: Article
Language:English
Published: MDPI AG 2024-11-01
Series:Mathematics
Subjects:
bladder tumor segmentation
deep learning
long-range dependencies
local details
attention mechanism
boundary enhancement and refinement
Online Access:https://www.mdpi.com/2227-7390/12/22/3580
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