## 3 Results
### Finetuning with Knowledge Distillation (FKD)  
Results of re-training with the dynamic and static teacher model are provided in Tables 4 and 5, respectively. For the dynamic model, the best segmentation performance for BAGLS-RT was achieved with batch size b = 25%. For BAGLS, the improvement was always similar being around 0.9%. In contrast, for the static model the best values were achieved with b = 100% (BAGLS-RT) and b = 50% (BAGLS). Training with batch size b = 25% showed the lowest improvement for BAGLS. Overall, the static model achieved slightly higher improvements for BAGLS than the dynamic model, since for the static model the parameters of the teacher model were not updated (i.e., remained as the baseline model) and should hence still be \"optimal\" for the BAGLS data set. In contrast, the improvements for BAGLS-RT were higher for the dynamic model, since the teacher model is continuously updated with additional information of BAGLS-RT.  
In summary, finetuning with dynamic knowledge distillation showed the best results over all re-training methods when considering segmentation performance for BAGLS and BAGLS-RT. The main advantage of this approach is that no old data have to be included in the re-training process in order to still consider old information when adjusting to new data. Additionally, new data may be continuously fed to the existing model (e.g., in several batches), allowing for fast adaption towards new recording modalities.  
Since the FKD approach showed the best re-training performance, additional results are provided separately for each camera system. Figure 10 shows the results of finetuning with knowledge distillation with a batch size of b = 100% (i.e., dynamic and static teacher model are equivalent) for each camera system in the BAGLS and BAGLS-RT datasets. Compared with the baseline model, FKD achieved an increase in segmentation performance for all camera systems with the exception of one camera in the BAGLS dataset. Overall, improvements in mIoU were larger for camera systems in the re-training dataset BAGLS-RT (0.92% to 3.88%) than for BAGLS (0.03% to 1.02%). The highest increase in performance (\(\Delta\)mIoU = 3.88%) could be observed for the Photron FASTCAM XA-S2 480K-M3, which was the only system used in combination with a flexible endoscope, yielding what are arguably the most novel data for the segmentation model, as videos recorded with flexible endoscopes were not included in BAGLS.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Training with Batch Size b**} & \multicolumn{3}{c}{**mIoU**} & \multicolumn{2}{c}{\(\Delta\)**mIoU (Baseline)**} \\ \cline{2-5}  & **BAGLS** & **BAGLS-RT** & **BAGLS** & **BAGLS-RT** \\ \hline b = 25\% & 0.7729 \(\pm\) 0.0002 & **0.7635 \(\pm\) 0.0024** & 0.87\% & **2.81\%** \\ b = 50\% & 0.7726 \(\pm\) 0.0048 & 0.7633 \(\pm\) 0.0033 & 0.84\% & 2.79\% \\ b = 100\% & **0.7732 \(\pm\) 0.0019** & 0.7566 \(\pm\) 0.0006 & **0.90\%** & 2.12\% \\ \hline \end{tabular}
\end{table}
Table 4: Segmentation performance of the U-Net after incremental finetuning with knowledge distillation and the dynamic teacher model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Training with Batch Size b**} & \multicolumn{3}{c}{**mIoU**} & \multicolumn{2}{c}{\(\Delta\)**mIoU (Baseline)**} \\ \cline{2-5}  & **BAGLS** & **BAGLS-RT** & **BAGLS** & **BAGLS-RT** \\ \hline b = 25\% & 0.7654 \(\pm\) 0.0055 & 0.7526 \(\pm\) 0.0059 & 0.12\% & 1.72\% \\ \hline b = 50\% & **0.7742 \(\pm\) 0.0034** & 0.7551 \(\pm\) 0.003 & **1.00\%** & 1.97\% \\ b = 100\% & 0.7732 \(\pm\) 0.0019 & **0.7566 \(\pm\) 0.0006** & 0.9\% & **2.12\%** \\ \hline \end{tabular}
\end{table}
Table 5: Segmentation performance of the U-Net after incremental finetuning with knowledge distillation and the static teacher model.