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CSWin-MDKDNet: cross-shaped window network with multi-dimensional fusion and knowledge distillation for medical image
Guanqing Cui1, Hongbin Lin2, Lijun Sun2
1School of Informatics, Xiamen University, Xiamen, 361005, China.
Scientific Reports
|March 2, 2026
Summary
This study introduces CSWin-MDKDNet, a deep learning model for medical image segmentation. It improves accuracy by balancing local details and global context, achieving state-of-the-art results on multiple datasets.
Area of Science:
- Computer Vision
- Medical Imaging Analysis
- Artificial Intelligence
Background:
- Deep learning significantly advances medical image segmentation, crucial for computer-aided diagnosis.
- Current methods struggle to balance local detail preservation and global context modeling.
Purpose of the Study:
- To propose CSWin-MDKDNet, a novel Transformer-based architecture for enhanced medical image segmentation.
- To address the limitations of existing models in feature fusion and redundancy.
Main Methods:
- Developed CSWin-MDKDNet, incorporating Multi-dimensional Selective Fusion (MDSF) and Knowledge Distillation Loss (KD-loss).
- MDSF utilizes channel-spatial attention for refined multi-scale feature fusion.
- KD-loss is employed to reduce feature redundancy in deep network layers.
Main Results:
- CSWin-MDKDNet achieved state-of-the-art performance on Synapse (multi-organ CT), ACDC (cardiac MRI), and ISIC2018 datasets.
- Achieved Dice Similarity Coefficient (DSC) scores of 81.82% on Synapse, 91.76% on ACDC, and 91.64% on ISIC2018.
- Demonstrated superior accuracy compared to existing medical image segmentation methods.
Conclusions:
- CSWin-MDKDNet effectively balances local detail and global context in medical image segmentation.
- The proposed architecture and techniques offer significant improvements for computer-aided diagnosis.
- The model shows strong potential for various medical imaging applications.
