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Palabras clave – provistas por la editorial

deep learning; segmentation; prostate; MRI; ENet; UNet; ERFNet; radiomics; gamma knife; imaging quantification; [11C]-methionine positron emission tomography; cancer; atrial fibrillation; 4D-flow; stasis; pulmonary vein ablation; convolutional neural network; transfer learning; maxillofacial fractures; computed tomography images; radiography; xenotransplant; cancer cells; zebrafish image analysis; in vivo assay; convolutional neural network (CNN); magnetic resonance imaging (MRI); neoadjuvant chemoradiation therapy (nCRT); pathologic complete response (pCR); rectal cancer; radiomics feature robustness; PET/MRI co-registration; image registration; fundus image; feature extraction; glomerular filtration rate; Gate’s method; renal depth; computed tomography; computer-aided diagnosis; medical-image analysis; automated prostate-volume estimation; abdominal ultrasound images; image-patch voting; soft tissue sarcoma; volume estimation; artificial intelligence; Basal Cell Carcinoma; skin lesion; classification; colon; positron emission tomography-computed tomography; nuclear medicine; image pre-processing; high-level synthesis; X-ray pre-processing; pipelined architecture; n/a