Contrastive image adaptation for acquisition shift reduction in medical imaging

Area of Science:

• Medical Imaging
• Computer Vision
• Machine Learning

Background:

• Domain shift, or acquisition shift, causes significant performance degradation in medical image analysis models.
• Existing methods struggle to effectively mitigate these differences between training and deployment.
• There is a critical need for advanced techniques to improve model robustness against varying imaging conditions.

Purpose of the Study:

• To develop an advanced method for unsupervised domain adaptation in medical imaging.
• To mitigate the impact of acquisition shift on deep learning models.
• To improve the generalization capabilities of medical image analysis techniques.

Main Methods:

• Proposed a cycle-free image-to-image architecture for unsupervised domain adaptation.
• Leveraged convolutional architectures to learn domain-agnostic features.
• Employed a combination of contrastive PatchNCE loss, adversarial loss, and edge-preserving loss.

Main Results:

• The proposed contrastive image adaptation approach effectively regularizes downstream deep supervised segmentation models.
• Demonstrated successful cross-modality synthesis under challenging conditions.
• The method shows promise even in low data regimes and with significant domain imbalance.

Conclusions:

• The developed contrastive image adaptation technique offers a robust solution for domain shift in medical imaging.
• This approach enhances the reliability and performance of AI models in diverse clinical settings.
• The method holds potential for improving various medical image analysis tasks, including segmentation and synthesis.