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METHOD AND SYSTEM FOR GENERATING MULTI-TASK LEARNING-TYPE GENERATIVE ADVERSARIAL NETWORK FOR LOW-DOSE PET RECONSTRUCTION
METHOD AND SYSTEM FOR GENERATING MULTI-TASK LEARNING-TYPE GENERATIVE ADVERSARIAL NETWORK FOR LOW-DOSE PET RECONSTRUCTION
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机译:用于低剂量PET重建的多任务学习型生成对抗网络的生成方法和系统
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摘要
The present application relates to a method and system for generating multi-task learning-type generative adversarial network for low-dose PET reconstruction, and relates to the field of deep learning. The method includes connecting layers of the encoder with layers of the decoder by skip connection to provide a U-Net type picture generator; generating a group of generative adversarial networks by matching a plurality of picture generators with a plurality of discriminators in one-to-one manner; obtaining a first multi-task learning-type generative adversarial network; designing a joint loss function 1 for improving image quality; and training the first multi-task learning-type generative adversarial network according to the joint loss function 1 in combination with an optimizer to provide a second multi-task learning-type generative adversarial network.
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