UnDA: Unpaired Domain Alignment for Cross-Modal Knowledge Transfer in Medical Imaging

TL;DR - UnDA transfers knowledge between unpaired medical-imaging modalities, avoiding the need for costly paired clinical data. It improves segmentation by aligning features while reducing supervision from uncertain source predictions.

• Uses backbone-agnostic attention pooling to extract semantically structured class tokens.
• Introduces uncertainty-weighted optimal transport to suppress noisy feature alignment.
• Applies per-class ProtoNCE with prototype memories to preserve global discriminability.
• Consistently improves target-modality segmentation accuracy and boundary precision in strictly unpaired evaluations.