Skin Lesion Segmentation Improved by Transformer-based Networks with
  Inter-scale Dependency Modeling
Sania Eskandari Janet Lumpp Luis Sanchez Giraldo
Melanoma, a dangerous type of skin cancer resulting from abnormal skin cell
growth, can be treated if detected early. Various approaches using Fully
Convolutional Networks (FCNs) have been proposed, with the U-Net architecture
being prominent To aid in its diagnosis through automatic skin lesion
segmentation. However, the symmetrical U-Net model's reliance on convolutional
operations hinders its ability to capture long-range dependencies crucial for
accurate medical image segmentation. Several Transformer-based U-Net topologies
have recently been created to overcome this limitation by replacing CNN blocks
with different Transformer modules to capture local and global representations.
Furthermore, the U-shaped structure is hampered by semantic gaps between the
encoder and decoder. This study intends to increase the network's feature
re-usability by carefully building the skip connection path. Integrating an
already calculated attention affinity within the skip connection path improves
the typical concatenation process utilized in the conventional skip connection
path. As a result, we propose a U-shaped hierarchical Transformer-based
structure for skin lesion segmentation and an Inter-scale Context Fusion (ISCF)
method that uses attention correlations in each stage of the encoder to
adaptively combine the contexts from each stage to mitigate semantic gaps. The
findings from two skin lesion segmentation benchmarks support the ISCF module's
applicability and effectiveness. The code is publicly available at
\url{https://github.com/saniaesk/skin-lesion-segmentation}