Dual-scale shifted window attention network for medical image segmentation.

ABSTRACT: Swin Transformer is an important work among all the attempts to reduce the computational complexity of Transformers while maintaining its excellent performance in computer vision. Window-based patch self-attention can use the local connectivity of the image features, and the shifted window-based patch self-attention enables the communication of information between different patches in the entire image scope. Through in-depth research on the effects of different sizes of shifted windows on the patch information communication efficiency, this article proposes a Dual-Scale Transformer with double-sized shifted window attention method. The proposed method surpasses CNN-based methods such as U-Net, AttenU-Net, ResU-Net, CE-Net by a considerable margin (Approximately 3% ∼ 6% increase), and outpe

SUBMITTER: Han DW 

PROVIDER: S-EPMC11291481 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature