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However, existing segmentation models face the challenges of inconsistency within classes and indistinguishability between classes because skin cancer lesions often exhibit variable scales, irregular shapes, blurred boundaries, and noises such as hair (Fi.
FigureSegmentation results of the baseline method (ResUnet) and the proposed method
In response to this problem, Yang Xiaodong's research group from Suzhou Institute of Medical Technology proposed a skin cancer lesion segmentation algorithm based on correlation learning between global and local pixe.
FigureSegmentation algorithm network structure
FigureComparison of segmentation results
The research result " ICL-Net: Global and Local Inter-pixel Correlations Learning Network for Skin Lesion Segmentation " has been published online in IEEE Journal of Biomedical and Health Informatics
Paper link: https://ieeexplo.
