Unsupervised and Self-supervised Learning in Low-Dose Computed Tomography Denoising: Insights from Training Strategies.
In recent years, X-ray low-dose computed tomography (LDCT) has garnered widespread attention due to its significant reduction in the risk of patient radiation exposure. However, LDCT images often contain a substantial amount of noises, adversely affecting diagnostic quality. To mitigate this, a plet...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 38; no. 2; pp. 902 - 931 |
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| Autores principales: | , , , , , , , |
| Formato: | algorithm diagnostic images equations & formulas pictorial review tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2025
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| Acceso en línea: | Ver este registro en EBSCOhost |