Abstract
Skin lesion segmentation in dermoscopic images is more challenging due to the irregular and blurred skin lesion boundaries, as well as the low visual contrast between the skin lesions and the surrounding normal tissues. This paper proposes a residual dense network for skin lesion segmentation in dermoscopy images. Compared with the existing mainstream image segmentation methods, we propose a novel residual dense module and increase the depth of the convolutional neural network, which not only makes the network easier to converge, but also enables each layer of the network to obtain sufficient information interaction.
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