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C MAL: cascaded network-guided class-balanced multi-prototype auxiliary learning for source-free domain adaptive
Wei Zhou1, Xuekun Yang2, Jianhang Ji3
1College of Computer Science, Shenyang Aerospace University, Shenyang, 110136, China. zhouweineu@outlook.com.
Medical & Biological Engineering & Computing
|January 20, 2025
Summary
This study introduces a new framework for source-free domain adaptation in medical imaging, improving model stability by generating accurate pseudo-labels and addressing class imbalance for better adaptation across datasets.
Area of Science:
- Medical Image Analysis
- Computer Vision
- Machine Learning
Background:
- Source-free domain adaptation (SFDA) is vital for medical image analysis, adapting models without labeled target data.
- Self-training methods in SFDA struggle with model instability from incorrect pseudo-labels and class imbalance.
Purpose of the Study:
- To enhance model stability in SFDA for medical imaging.
- To address challenges of pseudo-label accumulation and class imbalance in self-training SFDA.
Main Methods:
- Introduced a cascaded network-guided class-balanced multi-prototype auxiliary learning (C2MAL) framework.
- Employed a cascaded translation-segmentation network (CTS-Net) for accurate pseudo-label generation.
- Utilized a class-balanced multi-prototype auxiliary learning network (CMAL-Net) with novel loss functions for adaptation.
Main Results:
- C2MAL demonstrated superior performance over state-of-the-art methods on four benchmark fundus image datasets.
- The framework effectively improved model stability and adaptation, especially under significant domain shifts.
- Validated through extensive experiments on diverse medical imaging datasets.
Conclusions:
- The proposed C2MAL framework significantly enhances model stability and adaptation in SFDA for medical imaging.
- The novel approach effectively tackles pseudo-labeling issues and class imbalance, outperforming existing methods.
- C2MAL offers a robust solution for adapting medical image analysis models to new domains without labeled data.
