Inter-Scale Dependency Modeling for Skin Lesion Segmentation with
  Transformer-based Networks
Sania Eskandari Janet Lumpp
Melanoma is a dangerous form of skin cancer caused by the abnormal growth of
skin cells. Fully Convolutional Network (FCN) approaches, including the U-Net
architecture, can automatically segment skin lesions to aid diagnosis. The
symmetrical U-Net model has shown outstanding results, but its use of a
convolutional operation limits its ability to capture long-range dependencies,
which are essential for accurate medical image segmentation. In addition, the
U-shaped structure suffers from the semantic gaps between the encoder and
decoder. In this study, we developed and evaluated a U-shaped hierarchical
Transformer-based structure for skin lesion segmentation while we proposed an
Inter-scale Context Fusion (ISCF) to utilize the attention correlations in each
stage of the encoder to adaptively combine the contexts coming from each stage
to hinder the semantic gaps. The preliminary results of the skin lesion
segmentation benchmark endorse the applicability and efficacy of the ISCF
module.