Mostrando 5 resultados de: 5
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Progress in Biomedical Optics and Imaging - Proceedings of SPIE(2)
Artificial Intelligence in Medicine(1)
IISE Annual Conference and Expo 2018(1)
Neurocomputing(1)
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:googlescopusAdaResU-Net: Multiobjective adaptive convolutional neural network for medical image segmentation
ArticleAbstract: Adapting an existing convolutional neural network architecture to a specific dataset for medical imaPalabras claves:convolutional neural networks, deep learning, Evolutionary algorithms, Hyperparameter optimization, Medical image segmentation, Multiobjective optimizationAutores:Lai-Yuen S.K., Maria G. Baldeon CalistoFuentes:googlescopusEMONAS-Net: Efficient multiobjective neural architecture search using surrogate-assisted evolutionary algorithm for 3D medical image segmentation
ArticleAbstract: Deep learning plays a critical role in medical image segmentation. Nevertheless, manually designingPalabras claves:AutoML, convolutional neural networks, 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:googlescopusResU-Net: Residual convolutional neural network for prostate MRI segmentation
Conference ObjectAbstract: The identification and segmentation of the prostate on magnetic resonance images (MRI) can assist inPalabras claves:convolutional neural networks, deep learning, image segmentation, medical image processingAutores:Lai-Yuen S.K., Maria G. Baldeon CalistoFuentes:googlescopus