# Skin Lesion Segmentation by U-Net with Adaptive Skip Connection and Structural Awareness
## 5 Conclusions  
In this paper, based on the observation that the sizes of the skin lesion are varied across images and the low contrast between the lesion pigment and the healthy tissues, we propose the method that could learn the multi-scale information and the structural constraint simultaneously. We apply the Selective Kernel module into the skip connections of U-Net to transfer an appropriate and larger receptive field to the decoder, helping the model deal with the variation in lesion sizes. Moreover, we propose the auxiliary decoder of distance map delineation and contour detection with the purposed of acknowledging the skin lesion structure. The qualitative and quantitative results of our method prove its effectiveness in three public datasets. It can deal with the inconsistent appearance of skin lesions and solve several image segmentation problems. For future research, the segmentation result can be integrated to the lesion classification for further diagnosis of the detailed lesion area or even for prediction of the possible progression of the lesion. Skin lesion could be benign or malignant and, in the case of malignant tumor, early diagnosis is greatly recommended to avoid metastasis or even fatality. We will incorporate the clinical knowledge into the lesion image to automatically diagnose the states of the skin lesion with high precision.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & _JA_ & _DI_ & _AC_ \\ \hline Team-MtSinai [47] & 76.50 & 84.90 & 93.40 \\ Team-NLP LOGIX [48] & 76.20 & 84.70 & 93.20 \\ Team-BMIT [49] & 76.00 & 84.40 & 93.40 \\ PA-Net [50] & 77.60 & 85.80 & 93.60 \\ SkinNet [16] & 76.70 & 85.50 & 93.20 \\ [PERSON] et al. [51] & 76.80 & 86.20 & 94.50 \\ FrCN [52] & 77.11 & 87.08 & 94.03 \\ SLSDeep [17] & 78.20 & 87.80 & 93.60 \\ Proposed method & 79.95 & 87.61 & 94.55 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparison with other methods on the ISBI 2017 dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Training Data** & _JA_ & _DI_ & _AC_ \\ \hline [PERSON] et al. [53] & ISIC 2017 & 83.90 & 90.70 & 93.80 \\ FrCN [52] & ISIC 2017 & 84.79 & 91.77 & 95.08 \\ iMSCGnet [46] & ISIC 2017 & 88.21 & 93.36 & 95.71 \\ Proposed method & ISIC 2017 & 88.75 & 93.67 & 95.60 \\ DermoNet [54] & ISIC 2016 & 85.30 & 91.50 & - \\ [PERSON] et al. [45] & ISIC 2016 & 85.70 & 92.10 & 94.90 \\ DCL-PSI [55] & ISIC 2016 & 85.90 & 92.10 & 95.30 \\ Proposed method & ISIC 2016 & 89.22 & 94.04 & 96.01 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance comparison with other methods on the PH2 dataset.  
**Author Contributions:** Conceptualization, T.-D.-T.P.; Methodology, T.-D.-T.P.; Writing--review and editing, T.-D.-T.P. and G.-S.L.; Supervision, G.-S.L., S.-H.K. and H.-J.Y.; Project administration, G.-S.L., S.-H.K. and H.-J.Y.; Funding acquisition, G.-S.L., S.-H.K. and H.-J.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT). (NRF-2020R1A4A1019191) and also by the Bio & Medical Technology Development Program of the National Research Foundation (NRF) & funded by the Korean government (MSIT) (NRF-2019M3E5D1A02067961).  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.