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...
I tiakina i:
| Ngā kaituhi matua: | , , , , |
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| Hōputu: | Artigo |
| Reo: | Inglês |
| I whakaputaina: |
SpringerOpen
2024-02-01
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| Rangatū: | European Radiology Experimental |
| Ngā marau: | |
| Urunga tuihono: | https://doi.org/10.1186/s41747-023-00411-3 |
| Ngā Tūtohu: |
Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
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