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Palabras clave – provistas por la editorial

interpretable/explainable machine learning; image classification; image processing; machine learning models; white box; black box; cancer prediction; deep learning; multimodal learning; convolutional neural networks; autism; fMRI; texture analysis; melanoma; glcm matrix; machine learning; classifiers; explainability; explainable AI; XAI; medical imaging; diagnosis; ARMD; change detection; unsupervised learning; microwave breast imaging; image reconstruction; tumor detection; digital pathology; whole slide image processing; multiple instance learning; deep learning classification; HER2; medical images; transfer learning; optimizers; neo-adjuvant treatment; tumour cellularity; cancer; breast cancer; diagnostics; imaging; computation; artificial intelligence; 3D segmentation; active surface; discriminant analysis; PET imaging; medical image analysis; brain tumor; cervical cancer; colon cancer; lung cancer; computer vision; musculoskeletal images; lung disease detection; taxonomy; convolutional neural network; CycleGAN; data augmentation; dermoscopic images; domain transfer; macroscopic images; skin lesion segmentation; infection detection; COVID-19; X-ray images; bayesian inference; shifted-scaled dirichlet distribution; MCMC; gibbs sampling; object detection; surgical tools; open surgery; egocentric camera; computers in medicine; segmentation; MRI; ECG signal detection; portable monitoring devices; 1D-convolutional neural network; medical image segmentation; domain adaptation; meta-learning; U-Net; computed tomography (CT); magnetic resonance imaging (MRI); low-dose; sparse-angle; quantitative comparison; n/a