Enhancing diagnostic deep learning via self-supervised pretraining on large-scale, unlabeled non-medical images
Abstract Background Pretraining labeled datasets, like ImageNet, have become a technical standard in advanced medical image analysis. However, the emergence of self-supervised learning (SSL), which leverages unlabeled data to learn robust features, presents an opportunity to bypass the intensive lab...
Gorde:
| Egile Nagusiak: | , , , , |
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| Formatua: | Artigo |
| Hizkuntza: | Inglês |
| Argitaratua: |
SpringerOpen
2024-02-01
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| Saila: | European Radiology Experimental |
| Gaiak: | |
| Sarrera elektronikoa: | https://doi.org/10.1186/s41747-023-00411-3 |
| Etiketak: |
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