Anatomy-aware contrastive representation learning for fetal ultrasound
Self-supervised contrastive representation learning offers the advantage of learning meaningful visual representations from unlabeled medical datasets for transfer learning. However, applying current contrastive learning approaches to medical data without considering its domain-specific anatomical c...
| Auteurs principaux: | , , , , , |
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| Format: | Conference item |
| Langue: | English |
| Publié: |
Springer
2023
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