Segmentation of Lesions in Skin Image Based on Salient Object Detection with Deeply Supervised Learning

In this paper, a binary segmentation method HCDS based on salient object detection is proposed to solve the segmentation of lesions in skin images. The original U-Net model encounters some problems in its application to skin image segmentation, one of which is the non-pathological region information remained in the segmentation results. Our HCDS method improves its basic structure by adding a hybrid convolution module to the direct connection between the down-sampling and up-sampling. It deepens the abstract understanding of the input features in shallow layers and further eliminate the information interference of the non-pathological regions. Besides, the method employs a deeply supervised structure in each stage of up-sampling to learn from the output feature and ground truth. Finally, the HCDS integrates the multi-path outputs to obtain the best result. The experimental results show that the proposed method HCDS can effectively segment the lesion regions from the skin images. Our evaluation metrics about the HCDS are obviously superior to the original U-Net's.

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