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.
Proceedings of the National Academy of Sciences 117(25), 14453–14463 (2020) Detail-Enhancing Generative Adversarial Networks 11
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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.