# Skin Lesion Segmentation by U-Net with Adaptive Skip Connection and Structural Awareness
## 2 Related Works  
Skin lesion segmentation has been an intriguing matter of research for a long time. Traditional algorithms in this field revolved around three concepts: thresholding [3, 4], clustering [5, 6] and deformable contour model [7, 8]. Thresholding methods discriminate lesions and background by estimating the pixel intensity through image histogram. Clustering methods separate the two classes by learning the differences in the extrinsic characteristics. Deformable models initiate a curve surrounding the lesion and the curve evolves into the boundary of the object by mapping the chromatic changes. These methods extract low-level features like pixel values, color or contextual structure, which face the weakness of the variation in the appearance of skin lesions. It is arduous for clustering methods to deal with non-skin related noises or for thresholding, and for deformable methods to not be affected by the inconspicuous transition from pigmented region to the healthy skin tissues. The fact that they could not derive the semantic information from dermoscopic images restricts the skin lesion localization ability and the generalization on larger or partially dissimilar datasets. With the latest surge in deep neural networks, methods based on deep learning have been widely preferred thanks to the of semantic and latent feature acquisition ability and superior performance in myriad types of projects. Great success has been ascertained in medical image analysis such as optic disc segmentation [20], blood vessel segmentation [21], lung segmentation [22] and brain segmentation [23], and so is the task of skin lesion segmentation [9, 10, 11, 12, 13, 14, 15, 16, 17]. [PERSON] et al. [9] applied a fully convolutional network (FCN) [10] with a multistage learning method that, in the early-stage FCN, extracted the coarse low-level information and, in the late stage, learned the subtle characteristic of lesion boundaries. [PERSON] et al. [11] optimized their FCN with Jaccard distance loss. To obtain high-resolution predictions, [PERSON] et al. [12] proposed a dense deconvolutional network to learn rich features from local and global contextual information. [PERSON] et al. [13] extracted multiscale features from the layers of a residual network and aggregated them. [PERSON] et al. [14] compared the skin lesion segmentation performance between U-Net [15] and clustering, which observed the advantage of the former. Considering the global context feature extraction, SkinNet [16] integrated the dilated convolutions into the encoder branch of U-Net. In SLSDeep [17], the author introduced an encoder-decoder network with dilated residual and pyramid pooling networks for the coarse and fine representation of dermoscopic images. There are several networks that have been proposed in terms of enlarging the receptive field. [PERSON] et al. [24] proposed the pyramid pooling module in the Pyramid Scene Parsing network to obtain the global contextual prior along with the sub-region context and then concatenate different levels of features as the final global feature. In DeepLab [25], atrous convolution pyramid pooling analyzed the convolutional layers with filter at multiple sampling rates and effective fields-of-view, encoding the target object and the context of the image at multiple scales. In later years, the authors of [25] upgraded their paper to DeepLabv3+ [26], integrating the depthwise separable convolution into the atrous spatial pyramid pooling for faster and stronger network and reconsidering the decoder module for better object boundary recovery. The Dynamic Filter network [27] produced filters based on the input, and output the generated parameters to the next input, learning both spatial and photometric changes. To model the geometric transformation, Deformable Convolutional network [28] included deformable convolution which enables free-form deformation of the 2D sampling grid by the knowledge of preceding feature maps, and deformable ROI pooling, which enables the adaptation of object localization to different shapes.