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A Mathematical Theory of Phase-Consistent Information Bottleneck for Cross-Domain Generalization
1School of Information Engineering, Shandong Youth University of Political Science, Jinan 250103, China.
Entropy (Basel, Switzerland)
|July 28, 2026
Summary
This study introduces a novel mathematical framework using dual-tree complex wavelet transform (DTCWT) for domain generalization in medical image segmentation. It leverages phase and amplitude components to improve segmentation accuracy across different imaging domains.
Area of Science:
- Medical Image Analysis
- Computer Vision
- Signal Processing
Background:
- Domain generalization is crucial for medical image segmentation to ensure model performance across diverse datasets.
- Existing methods often struggle with variations in image acquisition and style.
- A robust framework is needed to handle domain shifts effectively.
Purpose of the Study:
- To propose a mathematical framework for domain generalization in medical image segmentation.
- To leverage the complementary nature of phase and amplitude components in dual-tree complex wavelet transform (DTCWT).
- To develop a method that improves segmentation accuracy across different medical imaging domains.
Main Methods:
- Developed a framework based on dual-tree complex wavelet transform (DTCWT) and variational information theory.
- Utilized a local phase-magnitude complementarity premise and an information bottleneck on structured subband representations.
- Incorporated a triple constraint mechanism (domain supervision, KL compression, orthogonality) and a predictive feature modulation scheme.
- Analyzed test-time adaptation using calibrated uncertainty and a two-pass inference strategy.
Main Results:
- Established theoretical results showing DTCWT amplitude subbands isolate domain-related information better than Fourier representations.
- Developed a variational information bottleneck encoder to compress domain-specific amplitude information.
- Demonstrated a predictive feature modulation scheme with O(1) spatial complexity.
- Showcased feasibility on public datasets (FeTS 2022, BraTS 2023) and derived conditions for reduced generalization gap.
Conclusions:
- The proposed framework offers a physically motivated approach to domain generalization in medical imaging.
- DTCWT phase and amplitude components can be effectively utilized for robust medical image segmentation.
- The framework demonstrates potential for improved generalization and reduced domain-specific variations.
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