# Multi-Task Learning for Medical Image Inpainting Based on Organ Boundary Awareness
## 4 Experiments and Results
### Results
These results are lower than the results of methods [4; 16]. When we change the binary cross-entropy loss with mean square error loss, our inpainting task results are a little bit better, but these results in the property of square-shaped mask are still lower than the method [16]. Finally, we replace the mean square error loss with Hinge loss. Our inpainting results outperform others, with 43.44 and 38.06 dB in the property of square-shaped and arbitrary-shaped masks, respectively. Figure 7 shows the qualitative comparison of inpainting results between using binary-cross entropy loss, L2 (mean square error) loss and Hinge loss in discriminators. For the best-generated results, we chose Hinge loss as our discriminators during training the model. Figure 8 introduces the qualitative comparison inpainting results between using the mono-task framework, multi-task with organ boundary knowledge framework, multi-task with edge knowledge framework, and multi-task with boundary combined with edge knowledge framework. We also examine the effect of SRM on our model. Table 5 shows the quantitative results of the proposed method between using SRM and without using it. The results show that generated images by the model with SRM are pretty much better in terms of either square-shaped or arbitrary-shaped masks. This proved the positive effect of SRM in making the model generate better high-resolution features with more useful details. From the above comparison and analysis, we find that the proposed model has superior performance in research on inpainting on medical CT images compared with other studies in recent years. Table 8 shows the detailed structure of the encoding part. The components of the discriminator network are introduced in Table 9. The detailed information of the decoding parts is given in Tables 10-12. Figure 7: Qualitative comparison inpainting results between using binary cross entropy loss, L2 loss and Hinge loss in discriminators. (**a**) Input masked image. (**b**) Result of using binary cross entropy loss. (**c**) Result of using L2 loss. (**d**) Result of using Hinge loss. (**e**) Ground truth image. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layer** & **Kernel Size** & **Stride** \\ \hline \hline \multirow{4}{*}{Deep Block} & ReflectionPad2d & \multirow{4}{*}{[7,7]} & \multirow{4}{*}{[1,1]} \\  & Conv2d+IN+ReLU & [7,7] & [1,1] \\ \hline \multirow{4}{*}{Deep Block} & Conv2d + IN & [8,8] & [1,1] \\  & Conv2d + IN & [8,8] & [1,1] \\  & Conv2d & [1,1] & [1,1] \\  & Conv2d & [1,1] & [1,1] \\ \hline \multirow{4}{*}{Deep Block} & Conv2d+IN+ReLU & [4,4] & [2,2] \\ \hline \multirow{4}{*}{Deep Block} & Conv2d + IN & [8,8] & [1,1] \\  & Conv2d + IN & [8,8] & [1,1] \\ \cline{1-1}  & Conv2d & [1,1] & [1,1] \\ \hline \multicolumn{4}{c}{8 \(\times\) DRN blocks} \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Architecture of encoding part in our network. Figure 8: Qualitative comparison inpainting results between using mono-task framework, multi-task with organ boundary knowledge framework, multi-task with edge knowledge framework and multi-task with boundary combined with edge knowledge framework. (**a**) Input masked image. (**b**) Result of using mono-task framework. (**c**) Result of using multi-task with organ boundary knowledge framework. (**d**) Result of using multi-task with edge knowledge framework. (**e**) Result of using multi-task with boundary combined with edge knowledge framework. (**f**) Ground truth image. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer** & **Kernel Size** & **Stride** \\ \hline \hline Conv2d + LeakyReLU & [4,4] & [2,2] \\ Conv2d + LeakyReLU & [4,4] & [2,2] \\ Conv2d + LeakyReLU & [4,4] & [2,2] \\ Conv2d + LeakyReLU & [4,4] & [1,1] \\ Conv2d & [4,4] & [1,1] \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Architecture of discriminator network.