Class-Aware Adversarial Transformers for Medical Image Segmentation.

ABSTRACT: Transformers have made remarkable progress towards modeling long-range dependencies within the medical image analysis domain. However, current transformer-based models suffer from several disadvantages: (1) existing methods fail to capture the important features of the images due to the naive tokenization scheme; (2) the models suffer from information loss because they only consider single-scale feature representations; and (3) the segmentation label maps generated by the models are not accurate enough without considering rich semantic contexts and anatomical textures. In this work, we present CASTformer, a novel type of adversarial transformers, for 2D medical image segmentation. First, we take advantage of the pyramid structure to construct multi-scale representations and handle multi-sc

SUBMITTER: You C 

PROVIDER: S-EPMC10395073 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature