Domain Adaptation for Medical Image Analysis: A Survey

Area of Science:

• Medical Image Analysis
• Machine Learning
• Computer-Aided Diagnosis

Background:

• Machine learning in medical imaging faces domain shift due to data distribution differences.
• Domain adaptation is a key solution for handling data heterogeneity in medical AI.

Purpose of the Study:

• To survey recent advances in domain adaptation for medical image analysis.
• To provide a comprehensive overview of domain adaptation techniques in this field.

Main Methods:

• Categorization of domain adaptation models into shallow and deep architectures.
• Classification of methods into supervised, semi-supervised, and unsupervised approaches.
• Review of benchmark medical image datasets used in domain adaptation research.

Main Results:

• Identified domain adaptation as crucial for robust medical image analysis.
• Detailed categorization of existing shallow and deep domain adaptation models.
• Summarized relevant datasets supporting domain adaptation research.

Conclusions:

• Domain adaptation is vital for overcoming domain shift in medical AI.
• The survey provides a structured understanding of current methods and future research directions.
• Highlights the need for continued research in domain adaptation for medical imaging.