The results show that the p-values for these differences are less than 0.001, indicating that the performance differences between the improved network and the baseline are highly significant, meaning that the effects of each newly elaborated component far exceed those caused by random fluctuations.
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AFCF-Net: A novel U-Net based asymmetric feature calibration and fusion network for skin lesion image segmentation.
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