Twisted Information Sharing Pattern-Based Multi-Branch Network for Semantic Segmentation of Medical Images
1. Overview
In the realm of medical imaging, where accurate semantic segmentation is of utmost importance, the emergence of TP-MNet has revolutionized the field. This innovative approach facilitates the seamless exchange of features among neighboring branches, overcoming the challenges posed by semantic isolation and enabling efficient feature fusion. TP-MNet incorporates advanced feature fusion modules, which play a pivotal role in capturing crucial lesion characteristics by leveraging comprehensive contextual semantic information. By harnessing the power of TP-MNet, medical practitioners and researchers are empowered with enhanced capabilities for precise and comprehensive analysis of medical images.
2. Network
3.Flow char of TP-MNet
4. Environment
The program runs on a high-performance server, with Pytorch version no less than 1.13.0 and Python version 3.8.15.