Inter-Scale Dependency Modeling for Skin Lesion Segmentation with Transformer-based Networks
This work addresses segmentation accuracy for melanoma diagnosis, representing an incremental improvement over existing methods.
The paper tackled the problem of semantic gaps and limited long-range dependency capture in U-Net-based skin lesion segmentation by proposing a U-shaped hierarchical Transformer structure with an Inter-scale Context Fusion (ISCF) module, achieving preliminary results that endorse its applicability and efficacy on a benchmark.
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.