Scale-wise discriminative region learning for medical image segmentation

Vision Transformer (ViT) has shown comparable capabilities to convolutional neural networks for medical image segmentation in recent years. However, most ViT-based models fail to effectively model long-range feature dependencies at multi-scales and ignore the crucial importance of the semantic richness of features at each scale for medical segmentation. To address this problem, we propose a novel Scale-wise Discriminative Region Learning Network (SDRL-Net) in this paper, which guides the model to focus on salient regions by differential modeling the global context relationships at each scale. In SDRL-Net, a scale-wise enhancement module is proposed to achieve more distinguishing feature representations in the encoder by concentrating spatially localized information and differentiated regional interactions simultaneously. Furthermore, we propose a multi-scale upsampling module that focuses on global multi-scale information through pyramid attention and then complements the local upsampling information to achieve better segmentation. Extensive experiments on three widely used public datasets demonstrate that our proposed SDRL-Net can perform excellently and outperform most state-of-the-art medical image segmentation methods. Code is available at https://github.com/MiniCoCo-be/SDRL-Net.