Titlebook: Simulation and Synthesis in Medical Imaging; 7th International Wo Can Zhao,David Svoboda,Maria Escobar Conference proceedings 2022 The Edit

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书目名称Simulation and Synthesis in Medical Imaging影响因子(影响力)




书目名称Simulation and Synthesis in Medical Imaging影响因子(影响力)学科排名




书目名称Simulation and Synthesis in Medical Imaging网络公开度




书目名称Simulation and Synthesis in Medical Imaging网络公开度学科排名




书目名称Simulation and Synthesis in Medical Imaging被引频次




书目名称Simulation and Synthesis in Medical Imaging被引频次学科排名




书目名称Simulation and Synthesis in Medical Imaging年度引用




书目名称Simulation and Synthesis in Medical Imaging年度引用学科排名




书目名称Simulation and Synthesis in Medical Imaging读者反馈




书目名称Simulation and Synthesis in Medical Imaging读者反馈学科排名

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,Bi-directional Synthesis of Pre- and Post-contrast MRI via Guided Feature Disentanglement,ment of contrast and image representations via a bi-directional image-to-image translation (I2I) model. Our proposed model can perform both pre-to-post and post-to-pre contrast synthesis, and provides an interpretable synthesis process by predicting contrast enhancement maps from the learned contras

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,Can Segmentation Models Be Trained with Fully Synthetically Generated Data?,c image generator. Our model can produce fully synthetic brain labels on-demand, with or without pathology of interest, and then generate a corresponding MRI image of an arbitrary guided style. Experiments show that brainSPADE synthetic data can be used to train segmentation models with performance

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,Multimodal Super Resolution with Dual Domain Loss and Gradient Guidance, HR input. The models were trained and evaluated on diverse datasets and performed comparably with MINet, another recently developed multimodal SR model, with approximately half the number of model parameters. The model generalized well to an external test set; performed satisfactorily on acquired L

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清洗

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,Backdoor Attack is a Devil in Federated GAN-Based Medical Image Synthesis,% of the original image size can corrupt the FedGAN model. Based on the proposed attack, we provide two effective defense strategies: global malicious detection and local training regularization. We show that combining the two defense strategies yields a robust medical image generation.