UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation

11 Dec 2019Zongwei ZhouMd Mahfuzur Rahman SiddiqueeNima TajbakhshJianming Liang

The state-of-the-art models for medical image segmentation are variants of U-Net and fully convolutional networks (FCN). Despite their success, these models have two limitations: (1) their optimal depth is apriori unknown, requiring extensive architecture search or inefficient ensemble of models of varying depths; and (2) their skip connections impose an unnecessarily restrictive fusion scheme, forcing aggregation only at the same-scale feature maps of the encoder and decoder sub-networks... (read more)

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