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UAE: Universal Anatomical Embedding on Multi-modality Medical Images

arXiv · · Significant research

CV Healthcare Research Ethics

Summary

Researchers propose a universal anatomical embedding (UAE) framework for medical image analysis to learn appearance, semantic, and cross-modality anatomical embeddings. UAE incorporates semantic embedding learning with prototypical contrastive loss, a fixed-point-based matching strategy, and an iterative approach for cross-modality embedding learning. The framework was evaluated on landmark detection, lesion tracking and CT-MRI registration tasks, outperforming existing state-of-the-art methods.

Keywords

medical imaging · anatomical embedding · cross-modality · landmark detection · self-supervised learning

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