A GAN with a U-Net discriminator, a channel-attention detail module, and class-conditional batch normalization synthesizes fUS images that outperforms baselines and improves downstream classification when used for data augmentation.
Nature communications 10(1), 1400 (2019)
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UltraDfeGAN: Detail-Enhancing Generative Adversarial Networks for High-Fidelity Functional Ultrasound Synthesis
A GAN with a U-Net discriminator, a channel-attention detail module, and class-conditional batch normalization synthesizes fUS images that outperforms baselines and improves downstream classification when used for data augmentation.