Deep learning with attention modules and residual transformations improves hepatocellular carcinoma (HCC) differentiation using multiphase CT.
Background: We hypothesize generative adversarial networks (GAN) combined with self‐attention (SA) and aggregated residual transformations (ResNeXt) perform better than conventional deep learning models in differentiating hepatocellular carcinoma (HCC). Attention modules facilitate concentrating on...
| Publicado en: | Precision Radiation Oncology Vol. 9; no. 1; pp. 13 - 23 |
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| Autores principales: | , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Mar2025
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| Acceso en línea: | Ver este registro en EBSCOhost |