Deep unsupervised clustering for prostate auto-segmentation with and without hydrogel spacer
Introduction. Clinical datasets for training deep learning (DL) models often exhibit high levels of heterogeneity due to differences such as patient characteristics, new medical techniques, and physician preferences. In recent years, hydrogel spacers have been used in some prostate cancer patients r...
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| Principais autores: | , , , , , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado: |
IOP Publishing
2025-01-01
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| Series: | Machine Learning: Science and Technology |
| Assuntos: | |
| Acceso en liña: | https://doi.org/10.1088/2632-2153/ada8f3 |
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