Attribute-Aware Adversarial Domain Augmentation for Zero-Shot Medical Domain Adaptation

Area of Science:

• Artificial Intelligence in Medicine
• Machine Learning for Healthcare
• Medical Image Analysis

Background:

• Deep learning models excel in medical diagnosis but fail in out-of-distribution (OOD) scenarios, leading to unreliable predictions for unseen patient data.
• Medical datasets exhibit domain diversity due to patient attributes (e.g., age), increasing the likelihood of encountering OOD instances.
• Domain generalization methods aim to bridge domain gaps without target data, but performance is limited by the lack of target domain knowledge.

Purpose of the Study:

• To address the limitations of current domain generalization techniques in medical diagnosis for OOD scenarios.
• To introduce a novel zero-shot domain adaptation approach that leverages prior knowledge of target domains.
• To enhance the reliability and accuracy of deep learning diagnostic models when applied to diverse patient populations.

Main Methods:

• Proposed Attribute-Aware Adversarial Domain Augmentation (AAADA), a method for generating synthetic data reflecting target domain characteristics.
• AAADA utilizes target attribute information to adversarially explore instances that capture target-specific features based on source attribute correlations.
• The approach enables diagnostic models to adapt decision boundaries effectively to unseen target domains without requiring direct target data access.

Main Results:

• AAADA significantly improved diagnostic accuracy on large-scale health check-up datasets compared to existing domain generalization methods.
• The method demonstrated effective utilization of target attribute information for enhancing model generalization.
• Experiments confirmed the superior performance of AAADA in handling OOD medical data.

Conclusions:

• Attribute-Aware Adversarial Domain Augmentation (AAADA) offers a promising solution for improving deep learning-based medical diagnosis in OOD settings.
• The zero-shot domain adaptation approach effectively leverages prior target attribute knowledge to generate informative synthetic data.
• AAADA enhances model robustness and accuracy, paving the way for more reliable AI-driven medical diagnostic tools across diverse patient groups.