A GAN with spatial pyramid pooling, kernel factorization, and squeeze-excitation blocks is applied to retinal vessel segmentation and reports better results than prior methods on DRIVE and STARE.
Expert Syst Appl 112, 229–242 (2018) Title Suppressed Due to Excessive Length 9
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Adversarial Learning with Multiscale Features and Kernel Factorization for Retinal Blood Vessel Segmentation
A GAN with spatial pyramid pooling, kernel factorization, and squeeze-excitation blocks is applied to retinal vessel segmentation and reports better results than prior methods on DRIVE and STARE.