Unsupervised Domain Adaptation for Segmentation with Black-box Source Model.

Area of Science:

• Computer Vision
• Machine Learning
• Medical Image Analysis

Background:

• Unsupervised Domain Adaptation (UDA) is crucial for transferring knowledge to new domains with limited labeled data.
• Privacy concerns and data access limitations hinder cross-center collaborations in traditional UDA.
• Existing UDA methods often require access to source domain data or a white-box source model.

Purpose of the Study:

• To propose a practical UDA solution for segmentation that overcomes data privacy and model access limitations.
• To enable knowledge transfer from a source domain using only a black-box segmentation model.
• To develop a privacy-preserving UDA framework for cross-domain collaborations.

Main Methods:

• A knowledge distillation scheme with exponential mixup decay (EMD) was employed to learn target-specific representations.
• Unsupervised entropy minimization was used for regularization of target domain confidence.
• The framework operates on a black-box source model, eliminating the need for source data or white-box access.

Main Results:

• The proposed framework achieved performance on par with white-box source model adaptation approaches.
• Effective knowledge transfer was demonstrated from the source to the target domain.
• The method successfully addressed privacy concerns associated with source data and models.

Conclusions:

• The developed UDA framework offers a practical and privacy-preserving solution for segmentation tasks.
• This approach facilitates cross-center and cross-domain collaborations by removing data sharing barriers.
• The method shows significant potential for real-world applications where data privacy is paramount.