GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification

Citation edge evidence

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• Generative adversarial network in medical imaging: A review→GAN-based synthetic medical image augmentation for increased CNN performance in liver les…Legacy edge — source detail unavailable
• GAN-based synthetic medical image augmentation for increased CNN performance in liver les…→Adam: A Method for Stochastic OptimizationLegacy edge — source detail unavailable
• GAN-based synthetic medical image augmentation for increased CNN performance in liver les…→Dropout: a simple way to prevent neural networks from overfittingLegacy edge — source detail unavailable
• GAN-based synthetic medical image augmentation for increased CNN performance in liver les…→Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Sh…Legacy edge — source detail unavailable
• GAN-based synthetic medical image augmentation for increased CNN performance in liver les…→A survey on deep learning in medical image analysisLegacy edge — source detail unavailable
• GAN-based synthetic medical image augmentation for increased CNN performance in liver les…→Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Excit…Legacy edge — source detail unavailable
• A survey on deep learning in medical image analysis→Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Excit…Legacy edge — source detail unavailable
• Generative adversarial network in medical imaging: A review→A survey on deep learning in medical image analysisLegacy edge — source detail unavailable
• Generative adversarial network in medical imaging: A review→Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Sh…Legacy edge — source detail unavailable
• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Sh…→Dropout: a simple way to prevent neural networks from overfittingLegacy edge — source detail unavailable