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.
science 313(5786), 504–507 (2006)
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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.