Deep learning for fully automated tumor segmentation and extraction of magnetic resonance radiomics features in cervical cancer.
Objective: To develop and evaluate the performance of U-Net for fully automated localization and segmentation of cervical tumors in magnetic resonance (MR) images and the robustness of extracting apparent diffusion coefficient (ADC) radiomics features.Methods: This retrospective study involved analy...
| Publicado en: | European Radiology Vol. 30; no. 3; pp. 1297 - 1306 |
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| Autores principales: | , , , , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
2020
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| Acceso en línea: | Ver este registro en EBSCOhost |