Early prediction of adverse outcomes in liver cirrhosis using a CT-based multimodal deep learning model.
Purpose: Early-stage cirrhosis frequently presents without symptoms, making timely identification of high-risk patients challenging. We aimed to develop a deep learning-based triple-modal fusion liver cirrhosis network (TMF-LCNet) for the prediction of adverse outcomes, offering a promising tool to...
| Publicado en: | Abdominal Radiology Vol. 51; no. 1; pp. 137 - 151 |
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| Autores principales: | , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jan2026
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| Acceso en línea: | Ver este registro en EBSCOhost |