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SelfReg-UNet: Self-Regularized UNet for Medical Image Segmentation
Wenhui Zhu1, Xiwen Chen2, Peijie Qiu3
1School of Computing and Augmented Intelligence, Arizona State University, AZ, USA.
Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|September 8, 2025
Summary
This study enhances UNet performance in medical image segmentation by addressing asymmetric supervision and feature redundancy. The proposed method balances encoder-decoder supervision and uses feature distillation for improved accuracy with minimal computational cost.
Area of Science:
- Medical Image Analysis
- Deep Learning Architectures
- Computer Vision
Background:
- UNet is a leading architecture for medical image segmentation.
- Existing UNet improvements often overlook in-depth analysis of learned patterns.
- Factors like asymmetric supervision and feature redundancy can impact UNet performance.
Purpose of the Study:
- To analyze UNet's learned patterns in medical image segmentation.
- To identify and address factors limiting UNet performance.
- To propose an improved UNet methodology for enhanced segmentation accuracy.
Main Methods:
- Investigated UNet's internal feature learning patterns.
- Proposed a method to balance supervision between UNet's encoder and decoder.
- Implemented feature distillation to reduce redundancy in feature maps.
- Integrated proposed enhancements as a plug-and-play module.
Main Results:
- The proposed method consistently improved standard UNet performance across four medical image segmentation datasets.
- Addressed issues of irrelevant feature learning due to asymmetric supervision.
- Reduced feature redundancy within the UNet architecture.
- Achieved performance gains with negligible computational overhead.
Conclusions:
- Balancing encoder-decoder supervision and reducing feature redundancy are key to improving UNet.
- The proposed feature distillation and supervision balancing method offers a simple yet effective enhancement for UNet.
- This approach provides a practical solution for boosting medical image segmentation accuracy using UNet.
