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.
In: 2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA)
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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.