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Dental Image Synthesis using Generative Adversarial Networks with Semantic Activation Blocks
Dental Image Synthesis using Generative Adversarial Networks with Semantic Activation Blocks
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机译:牙科图像合成使用具有语义激活块的生成对抗性网络
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摘要
A GAN is trained to process input images and produce a synthetic dental image. The GAN further takes masks as inputs with each image, the masks labeling pixels of the image corresponding to dental features (anatomy and/or treatments). The GAN includes an encoder-decoder with normalization between stages of the decoder according to the masks. A synthetic image and an unpaired dental image is evaluated by a first discriminator of the GAN to obtain a realism estimate. The synthetic image and an unpaired dental image may be processed using a pretrained dental encoder to obtain a perceptual loss. The GAN is trained with the realism estimate and perceptual loss. Utilization may include modifying a mask for an input image to include or exclude a shape of a feature such that the synthetic image includes or excludes a dental feature.
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