SAASN uses self-attention and SSIM-based cycle losses in a GAN to translate multiple stain appearances into a common domain, with reported SSIM gains over existing methods.
In: Medical Imaging 2014: Digital Pathology
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Self-Attentive Adversarial Stain Normalization
SAASN uses self-attention and SSIM-based cycle losses in a GAN to translate multiple stain appearances into a common domain, with reported SSIM gains over existing methods.