# Adaptation to CT Reconstruction Kernels by Enforcing Cross-Domain Feature Maps Consistency
## 5 Results
### Trade-Off between Consistency and Segmentation Quality  
The main problem with maximizing the consistency score is converging to the trivial solution (empty predictions). We vary \(\alpha\), the parameter that balances the consistency and segmentation losses, for P-Consistency and F-Consistency; see Figure 4. The resulting trade-off follows the expected trend: consistency increases to 1 and target Dice Score decreases to 0 with increasing \(\alpha\). \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & \multirow{2}{*}{**COVID-Train**} & \multirow{2}{*}{**COVID-Test**} & \multicolumn{6}{c}{**Paired-Private Consistency**} \\ \cline{4-8}  & & & **FC07/55** & **FC07/51** & **SOFT/LUNG** & **STAND/LUNG** & **Mean** \\ \hline Baseline & \(\mathbf{0.60\pm 0.04}\) & \(0.56\pm 0.03\) & \(0.52\pm 0.06\) & \(0.39\pm 0.07\) & \(0.58\pm 0.08\) & \(0.28\pm 0.05\) & \(0.46\pm 0.05\) \\ \hline DANN (4) & \(0.58\pm 0.05\) & \(\mathbf{0.64\pm 0.02}\) & \(0.84\pm 0.02\) & \(0.70\pm 0.02\) & \(\mathbf{0.86\pm 0.03}\) & \(0.66\pm 0.06\) & \(0.78\pm 0.02\) \\ DANN (2) & \(0.59\pm 0.05\) & \(\mathbf{0.64\pm 0.02}\) & \(0.81\pm 0.03\) & \(0.70\pm 0.03\) & \(0.74\pm 0.02\) & \(0.58\pm 0.07\) & \(0.73\pm 0.02\) \\ \hline P-Cons (4) & \(0.59\pm 0.04\) & \(0.61\pm 0.01\) & \(0.65\pm 0.05\) & \(0.60\pm 0.02\) & \(0.77\pm 0.01\) & \(0.47\pm 0.04\) & \(0.63\pm 0.03\) \\ P-Cons (2) & \(0.59\pm 0.04\) & \(0.59\pm 0.03\) & \(0.62\pm 0.03\) & \(0.56\pm 0.02\) & \(0.72\pm 0.01\) & \(0.40\pm 0.04\) & \(0.59\pm 0.02\) \\ \hline F-Cons (4) & \(0.57\pm 0.03\) & \(\mathbf{0.64\pm 0.03}\) & \(\mathbf{0.88\pm 0.01}\) & \(\mathbf{0.72\pm 0.04}\) & \(0.83\pm 0.02\) & \(\mathbf{0.70\pm 0.05}\) & \(\mathbf{0.80\pm 0.01}\) \\ F-Cons (2) & \(0.58\pm 0.04\) & \(\mathbf{0.64\pm 0.01}\) & \(0.83\pm 0.02\) & \(0.64\pm 0.03\) & \(0.75\pm 0.02\) & \(0.59\pm 0.02\) & \(0.73\pm 0.01\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of DANN, P-Consistency, and F-Consistency generalizing to previously unseen SOFT, STANDARD, and LUNG kernels. The numbers in the brackets next to the methods correspond to the number of kernel pairs in the Paired-private dataset they are trained with, e.g., DANN (4) matches with the DANN in Table 3. All results are Dice Scores in the format _mean_\(\pm\)_std_ calculated from 5-fold cross-validation. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & \multirow{2}{*}{**COVID-Train**} & \multirow{2}{*}{**COVID-Test**} & \multicolumn{6}{c}{**Paired-Private Consistency**} \\ \cline{4-8}  & & & **FC07/55** & **FC07/51** & **LUNG/SOFT** & **LUNG/STAND** & **Mean** \\ \hline Baseline & \(\mathbf{0.60\pm 0.04}\) & \(0.56\pm 0.03\) & \(0.52\pm 0.06\) & \(0.39\pm 0.07\) & \(0.58\pm 0.08\) & \(0.28\pm 0.05\) & \(0.46\pm 0.05\) \\ \hline DANN & \(\mathbf{0.60\pm 0.03}\) & \(\mathbf{0.64\pm 0.02}\) & \(0.75\pm 0.02\) & \(\mathbf{0.64\pm 0.05}\) & \(0.67\pm 0.03\) & \(0.50\pm 0.05\) & \(0.66\pm 0.02\) \\ \hline P-Cons & \(0.53\pm 0.03\) & \(0.58\pm 0.03\) & \(0.54\pm 0.05\) & \(0.44\pm 0.03\) & \(0.57\pm 0.04\) & \(0.28\pm 0.06\) & \(0.47\pm 0.03\) \\ \hline F-Cons & \(0.59\pm 0.04\) & \(\mathbf{0.64\pm 0.02}\) & \(\mathbf{0.80\pm 0.02}\) & \(\mathbf{0.63\pm 0.04}\) & \(\mathbf{0.71\pm 0.02}\) & \(\mathbf{0.55\pm 0.05}\) & \(\mathbf{0.70\pm 0.02}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of all adaptation methods from Table 3 except FBPAug trained on the Public-paired dataset. All results are Dice Scores in the format _mean_\(\pm\)_std_ calculated from 5-fold cross-validation. We highlight the consistency scores near or below Baseline level in _italic_. The best consistency scores are highlighted in **bold**. We use Dice Score on the COVID-train dataset as a perceptual criterion to choose \(\alpha\). We stop at the largest \(\alpha\) value before Dice Score starts to drop: \(10^{-3}\) for P-Consistency and 1 for F-Consistency. However, we use Paired-private, which participates in the final comparison, to calculate the consistency score.