# Skin Lesion Segmentation by U-Net with Adaptive Skip Connection and Structural Awareness
## 1 Introduction  
Skin lesions could be recognized as patches with darkened pigment or redness compared to the overall skin appearance. In most cases, they are harmless, but they may be indications of melanoma, which is a type of skin cancer. Although melanoma is among the most common cancers, the survival rate is high, at 92% [1], as long as it does not reach the late stage of cancer. The severity of melanoma is proportional to the detection time from the emergence of skin lesion or its metastasis to other organs, such as the lung or brain. Therefore, early-stage diagnosis of melanoma is critical for success in cancer treatment. The conventional method of removing the malignant lesions from the skin is surgical excision. Before the main treatment, skin lesion's condition is recorded as dermoscopic images, which visualize the impaired skin area in high resolution and detail. Subsequently, this image is scrutinized in order to locate the accurate area of lesion and diagnose whether it is dangerous. The preparation step was carried out manually in the past by experienced dermatologists, but this was time-consuming and the clinical inspections from individuals were subjective and inconsistent. There are requirements for an automatic inspection system that has the knowledge and follows the standard rules of skin evaluation, and could deal with an increasing number of dermoscopic images. Recently, computer-aided diagnosis (CAD) systems have been built up as human assistance and shown a promising ability in both skin lesion segmentation and classification. Skin lesion segmentation is a key step, prior to the skin problem categorization. In the past, researchers came up with solutions based on classical techniques like thresholding, clustering, edge and region detection [2; 3; 4; 5; 6; 7; 8]. They are able to deal with many apparent cases of different types of skin, but there are existing impediments that these methodscould not overcome. Hence, for the past decade, CAD systems for this task have favored the efficiency of deep learning methods, contributing a lot of novel approaches and positive results [9; 10; 11; 12; 13; 14; 15; 16; 17]. The architecture of the deep-learning methods used to separate the concerned area from healthy skin generally consists of an encoder and a decoder. The former extracts both the low-level and high-level features, while the latter exploits the latent representation and interprets it as a desirable outcome. Despite the undisputed capacity, deep learning methods still encounter some of the intrinsic and artificial problems of skin lesion segmentation, as briefly shown in Figure 1. The first one is the variability in the size of lesions in dermoscopic images. Some skin lesions are too small compared to the whole image and some occupy the entire image, with little unaffected skin remaining. Another problem is the heterogeneity of the shape and color. Skin pigmentation ordinarily differs from one person to another, and so do the pigments of lesions. In some cases, the contrast between lesion and healthy skin is insignificant and suffers from artifacts and obstructions such as hair, artificial marks and air bubbles. Furthermore, lesion detection becomes entangled with the capricious annotation from experts. There is no agreement among dermatologists, so the lesion delineation could either be loose or excessively detailed, causing deep learning methods to achieve the same performance over the dataset. The aforementioned characteristics of dermoscopic images make this task challenging, even with the impressive discriminative abilities of deep neural networks. To tackle those problems, CAD systems must comprehend not only the pigmented areas, but also the adjacent regions of healthy tissues. They must grasp the discriminative features that could convey the foreground and background and the contextual information that could reproduce the structure of the lesion. In this paper, we propose a new deep neural network based on the U-net architecture [15] for the skin lesion segmentation.