# Skin Lesion Segmentation by U-Net with Adaptive Skip Connection and Structural Awareness
## 4 Experiments and Results
### Results  
#### 4.4.1 Ablation Studies  
To evaluate the effectiveness of the proposed method on skin lesion segmentation, we conduct the ablation studies on the ISBI 2016 and ISBI 2017 datasets. The DenseNet169 model is set as the baseline benchmark and we subsequently append other configurations to it, to verify their supportive impact on the baseline model. The detailed quantitative experimental results for the ISBI 2016 and ISBI 2017 datasets are displayed in Table 1 with the accompanying methods, namely, the SK module in the skip connections, structural awareness module as the additional decoder, deep supervision module at the end of the main decoder and TTA as the post-processing step. By incorporating the new modules, we observe the increase in \(JA\), \(DI\) and \(AC\) in both datasets. The baseline model has a greater boost with the assistance of the structural awareness than with the addition of the SK module. The former enhances the \(DI\) by 0.67 and 1.18, while the latter enhances the \(DI\) score by 0.4 and 0.92 on the ISBI 2016 and ISBI 2017, respectively. The combination of two aforementioned methods and the deep supervision module achieves the superior performance to the baseline model, with the \(DI\) increase of 0.84 and 1.82 on each dataset. The post-processing step TTA further improves the performance of our model, obtaining the final \(DI\) results of 92.61 for the ISBI 2016 dataset and 87.61 for the ISBI 2017. In conclusion, Table 1 proves that the additional modules along with their abilities to extract the contextual and multi-scale information obtain better segmentation results and the coordination among auxiliary modules does not conflict each other.  
We also implement a qualitative analysis on the effectiveness of the proposed methods on skin lesion segmentation. Figure 4 shows the experimental results of several challenging cases. Compared to the baseline segmentation results, the SK module clearly expands the receptive field, since the prediction area is broader while the structure module delineates the boundary that is closer to boundary of the ground truth. The baseline model could discern the noticeable discrepancy between the impaired and healthy tissues but the proposed method gains a deeper understanding of the texture of the lesions. The SK module may produce the lesion map that excessively spread over a larger region than expected (Figure 4, second image in third row) but when it is associated with the structural module, its influence is appropriately weakened to satisfy the two mentioned problems. In the cases of low contrast, we see that our model performs well in keeping track of the lesion pattern and produce a satisfactory lesion map (Figure 4, fourth row). The object localization capability is obviously enhanced in the later implementations, where the prediction areas outside the lesions are eliminated (Figure 4, last row).  
To examine the effectiveness of applying different kernel sizes in the SK module, we conduct the experiments with four types of kernel combination on the ISBI 2017 dataset, as shown in Table 2. As we stated in the above section, a large receptive field does not always promise best performance. Among the kernel size configurations, the combination of kernel size 3 \(\times\) 3 and kernel size 5 \(\times\) 5 produces the best segmentation performance, which is 87.61 in _DI_. Surprisingly, the worst segmentation performance is from the combination of triple kernel size, which obtains a _DI_ result of only 86.71.