Unsupervised domain adaptation for medical image segmentation using adaptogen-perturbation.

Area of Science:

• Medical Imaging
• Machine Learning
• Computer Vision

Background:

• Domain shift in medical AI hinders pre-trained model application due to variations in devices and patients.
• Existing domain adaptation methods often require single-target adaptation or data sharing, posing privacy concerns in healthcare.
• Unsupervised domain adaptation is crucial for adapting models to diverse clinical settings without compromising patient privacy.

Purpose of the Study:

• To propose a novel unsupervised multi-target domain adaptation method for medical applications.
• To address the challenge of domain shift without requiring sensitive patient data sharing.
• To enhance the robustness and generalizability of pre-trained models in clinical settings.

Main Methods:

• Introduced an Adaptogen-Perturbation (AP) signal to bridge source and target domain gaps.
• Injected the optimized AP into latent features to facilitate model adaptation.
• Developed a Spectral/Geometric Consistency learning framework for unsupervised AP optimization.

Main Results:

• The proposed method effectively adapts pre-trained models to multiple target domains without data sharing.
• Adaptogen-Perturbation (AP) demonstrated significant improvements in medical image segmentation tasks.
• The Spectral/Geometric Consistency framework enhanced model robustness against variations.

Conclusions:

• The novel unsupervised multi-target domain adaptation method is effective for medical AI.
• Adaptogen-Perturbation (AP) offers a privacy-preserving solution for domain adaptation in healthcare.
• This approach advances the application of AI in diverse clinical environments.