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C-MADA: Unsupervised Cross-Modality Adversarial Domain Adaptation framework for Medical Image Segmentation
Conference ObjectAbstract: Deep learning models have obtained state-of-the-art results for medical image analysis. However, CNNPalabras claves:Domain Adaptation, Generative Adversarial Networks, image segmentation, Medical image analysis, unsupervised learningAutores:Lai-Yuen S.K., Maria G. Baldeon CalistoFuentes:googlescopusEMONAS: Efficient multiobjective neural architecture search framework for 3D medical image segmentation
Conference ObjectAbstract: Deep learning plays a critical role in medical image segmentation. Nevertheless, manually designingPalabras claves:deep learning, Hyperparameter optimization, Medical image segmentation, Multiobjective optimization, Neural Architecture SearchAutores:Lai-Yuen S.K., Maria G. Baldeon CalistoFuentes:googlescopusSelf-adaptive 2D-3D ensemble of fully convolutional networks for medical image segmentation
Conference ObjectAbstract: Segmentation is a critical step in medical image analysis. Fully Convolutional Networks (FCNs) havePalabras claves:deep learning, Hyperparameter optimization, Medical image segmentation, Multiobjective optimization, Neural Architecture SearchAutores:Lai-Yuen S.K., Maria G. Baldeon CalistoFuentes:googlescopus