A Chebyshev Confidence Guided Source-Free Domain Adaptation Framework for Medical Image Segmentation

Area of Science:

• Artificial Intelligence
• Computer Vision
• Medical Imaging

Background:

• Source-free domain adaptation (SFDA) is crucial for medical imaging due to data heterogeneity and privacy concerns.
• Current SFDA methods often rely on pseudo-labels (PLs) generated through self-training, which can be inaccurate due to domain shift.
• The deterioration of PL accuracy limits the effectiveness of existing SFDA techniques.

Purpose of the Study:

• To develop a novel SFDA framework that accurately assesses and improves the reliability of pseudo-labels (PLs).
• To enhance the performance of SFDA in medical imaging by generating high-quality, self-improving PLs for self-training.

Main Methods:

• Proposed a Chebyshev confidence guided SFDA framework to estimate PL reliability by calculating the probability lower bound of PL confidence.
• Introduced two confidence-guided denoising methods: direct denoising and prototypical denoising.
• Developed a teacher-student joint training scheme (TJTS) with a confidence weighting module for iterative PL improvement.

Main Results:

• The proposed framework effectively assesses PL reliability and generates self-improving PLs.
• Confidence-guided denoising and TJTS prevent noise propagation and enhance PL accuracy.
• Extensive experiments demonstrate the framework's superiority over state-of-the-art SFDA methods across diverse domains.

Conclusions:

• The Chebyshev confidence guided SFDA framework provides a novel approach for reliable PL estimation.
• The developed methods significantly improve PL quality, leading to enhanced SFDA performance.
• This work offers a robust solution for domain adaptation in medical imaging, addressing key limitations of current methods.