A Transformer Network Architecture for Dermoscopy Image Segmentation

Abstract

Aiming at the problems of irregular shape and blurred boundary of skin lesions in skin lesions images, this paper proposes a skin lesion segmentation algorithm combining CNN and Transformer. Firstly, Resnet is used as the backbone feature extraction network to extract features, and the extracted feature map sequence is used as the input of Transformer. A new structural boundary attention gate is added to Transformer to extract enough local details to deal with fuzzy boundaries. Finally, DenseASPP is used to enhance features Represents and processes multi-scale information, and proposes an improved loss function, the purpose of which is to make the model pay attention to the boundary region when calculating the loss function. The experimental results show that the dice value and JI value of the network on the ISIC2017 dataset are 0.854534 and 0.767901, respectively, and the dice value and JI value on the ISIC2018 dataset are 0.908548 and 0.843689, respectively, which achieves good results compared to other advanced models. Its effectiveness is proved by comparing with different models and showing the effect.