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