# Skin Lesion Segmentation by U-Net with Adaptive Skip Connection and Structural Awareness
## 4 Experiments and Results
### Results
#### 4.4.2 Comparison to Other Published Methods  
In this section, we analyze the performance of our method on the ISBI 2016, ISBI 2017 and PH2 datasets. Our results are compared to the teams participating in the ISBI challenges and other published methods. The results are taken from their publications. Tables 3-5 show the segmentation performance on the ISBI 2016, ISBI 2017 and PH2 datasets, respectively.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**2016**} & \multicolumn{3}{c}{**2017**} \\ \cline{2-7}  & _JA_ & _DI_ & _AC_ & _JA_ & _DI_ & _AC_ \\ \hline Baseline & 85.41 & 91.55 & 95.80 & 77.09 & 85.40 & 93.94 \\ Baseline + SK & 86.20 & 91.95 & 96.18 & 78.23 & 86.32 & 93.96 \\ Baseline + structural awareness & 86.43 & 92.22 & 96.25 & 78.67 & 86.58 & 94.17 \\ Baseline + SK + structural awareness & 86.54 & 92.28 & 96.31 & 79.04 & 86.85 & 94.32 \\ Baseline + SK + structural awareness + deep supervision & 86.74 & 92.39 & 96.39 & 79.36 & 87.22 & 94.41 \\ Baseline + SK + structural awareness + deep supervision + TTA & 87.08 & 92.61 & 96.48 & 79.95 & 87.61 & 94.55 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Ablation studies on the ISBI 2016 and ISBI 2017 datasets.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**(3 \(\times\) 3)** & **(5 \(\times\) 5)** & **(7 \(\times\) 7)** & **JA** & **DI** & **AC** \\ \hline ✓ & ✓ & & 79.95 & 87.61 & 94.55 \\ ✓ & & ✓ & 78.79 & 86.82 & 94.26 \\  & ✓ & ✓ & 79.25 & 86.97 & 94.39 \\ ✓ & ✓ & ✓ & 78.70 & 86.74 & 94.18 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison of different kernel sizes in the SK module.  
Figure 4: Qualitative segmentation result comparison with different configurations. The green lines indicate the location and the area of the ground truth of the skin lesion and the red lines demonstrate the prediction of our method on the same input. From the first column to the sixth columns: the baseline method, baseline + SK module, baseline + structural awareness, baseline + SK + structural awareness, the proposed method and itself after TTA as post-processing step.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & _JA_ & _DI_ & _AC_ \\ \hline Team-EXB [40] & 84.30 & 91.00 & 95.30 \\ Team-CUMED [41] & 82.90 & 89.70 & 94.90 \\ Team-Rahman [42] & 82.20 & 89.50 & 95.20 \\ [PERSON] et al. [43] & 84.10 & 90.70 & 95.30 \\ [PERSON] et al. [11] & 84.70 & 91.20 & 95.50 \\ [PERSON] et al. [9] & 84.64 & 91.18 & 95.91 \\ [PERSON] et al. [44] & 85.50 & 91.90 & 95.70 \\ [PERSON] et al. [45] & 85.80 & 91.80 & 93.80 \\ iMSCGnet [46] & 85.92 & 91.91 & 96.08 \\ Proposed method & 87.08 & 92.61 & 96.48 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison with other methods on the ISBI 2016 dataset.  
In the ISBI 2016 dataset, we achieve the best segmentation performance in _JA_, _DI_ and _AC_. Based on the _JA_ and _DI_ results, we see that the proposed method is more capable of locating the lesion and find the neighboring elements than other methods.  
In the ISBI 2017 dataset, we achieve the best segmentation performance in _JA_ and _AC_ but not in _DI_. Our results prove that the proposed method is consistent in both datasets. The ISBI 2017 dataset is more difficult for segmentation, since there are more sophisticated skin lesions in melanoma cases.  
In the PH2 dataset, we conduct two experiments on the pretrained model of the ISBI 2016 and the ISBI 2017, which other methods also followed, because this dataset is small. We achieve the best performance on the PH2 dataset in both cases. The result on the trained dataset of ISBI 2017 is expected to acquire a better performance, since the ISBI 2017 dataset is bigger. However, the trained model on the ISBI 2016 dataset generalizes better on the PH2 dataset.