  Accurate segmentation of skin lesions in dermatoscopic images is crucial for
the early diagnosis of skin cancer and improving the survival rate of patients.
However, it is still a challenging task due to the irregularity of lesion
areas, the fuzziness of boundaries, and other complex interference factors. In
this paper, a novel LCAUnet is proposed to improve the ability of complementary
representation with fusion of edge and body features, which are often paid
little attentions in traditional methods. First, two separate branches are set
for edge and body segmentation with CNNs and Transformer based architecture
respectively. Then, LCAF module is utilized to fuse feature maps of edge and
body of the same level by local cross-attention operation in encoder stage.
Furthermore, PGMF module is embedded for feature integration with prior guided
multi-scale adaption. Comprehensive experiments on public available dataset
ISIC 2017, ISIC 2018, and PH2 demonstrate that LCAUnet outperforms most
state-of-the-art methods. The ablation studies also verify the effectiveness of
the proposed fusion techniques.
