Self-supervised contrastive video-speech representation learning for ultrasound
In medical imaging, manual annotations can be expensive to acquire and sometimes infeasible to access, making conventional deep learning-based models difficult to scale. As a result, it would be beneficial if useful representations could be derived from raw data without the need for manual annotatio...
| Main Authors: | , , , , , |
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| Format: | Conference item |
| Language: | English |
| Published: |
Springer
2020
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