Enhanced Lung Cancer Survival Prediction Using Semi-Supervised Pseudo-Labeling and Learning from Diverse PET/CT Datasets.
Simple Summary: This study presents a novel semi-supervised learning (SSL) approach that improves lung cancer survival predictions by incorporating diverse datasets, including head and neck cancer (HNCa), alongside handcrafted and deep radiomic features (HRF/DRF) from PET/CT scans. By shifting from...
| Publicado en: | Cancers Vol. 17; no. 2; pp. 285 - 304 |
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| Autores principales: | , , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
MDPI
Jan2025
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| Acceso en línea: | Ver este registro en EBSCOhost |