USCT-UNet: Rethinking the Semantic Gap in U-Net Network from U-shaped Skip Connections with Multichannel Fusion Transformer

Abstract:

Medical image segmentation is a crucial component of computer-aided clinical diagnosis, with state-of-the-art models often being variants of U-Net. Despite their success, these models’ skip connections introduce an unnecessary semantic gap between the encoder and decoder, which hinders their ability to achieve the high precision required for clinical applications. Awareness …