## 3 Materials and Methods
### Experimental Setup
#### 3.2.3 Hyperparameter Optimization  
Hyperparameters are variables that control the training process and the structure of the model. A model's performance can vary significantly depending on the selected hyperparameters, and they must be configured before the training phase.  
The hyperparameters related to the network architecture were set as follows: the dropout of the network given in Figure 1 was set to 0.3, and the learning rate for the  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Plane** & **Mask Size** & **Training** & **Validation** & **Testing** & **Total** \\ \hline \multirow{4}{*}{Axial} & Small & 190 & 48 & 98 & 336 \\  & Medium Down & 189 & 48 & 105 & 342 \\  & Medium Up & 190 & 47 & 102 & 339 \\  & Large & 190 & 47 & 102 & 339 \\  & **Total** & 759 & 190 & 407 & 1356 \\ \hline \multirow{4}{*}{Coronal} & Small & 237 & 59 & 127 & 423 \\  & Medium Down & 239 & 60 & 128 & 427 \\  & Medium Up & 238 & 60 & 127 & 425 \\  & Large & 239 & 59 & 128 & 426 \\  & **Total** & 953 & 238 & 510 & 1701 \\ \hline \multirow{4}{*}{Sagittal} & Small & 142 & 36 & 76 & 254 \\  & Medium Down & 143 & 36 & 76 & 255 \\  & Medium Up & 142 & 36 & 76 & 254 \\  & Large & 143 & 35 & 77 & 255 \\  & **Total** & 570 & 143 & 305 & 1018 \\ \hline \end{tabular}
\end{table}
Table 1: Number of samples for training, validation, and testing using ISLES 2015 in different planes.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Plane** & **Mask Size** & **Training** & **Validation** & **Testing** & **Total** \\ \hline \multirow{4}{*}{Axial} & Small & 670 & 168 & 359 & 1197 \\  & Medium Down & 681 & 170 & 365 & 1216 \\  & Medium Up & 675 & 169 & 362 & 1206 \\  & Large & 677 & 169 & 362 & 1208 \\  & **Total** & 2703 & 676 & 1448 & 4827 \\ \hline \multirow{4}{*}{Coronal} & Small & 1060 & 264 & 568 & 1892 \\  & Medium Down & 1166 & 292 & 625 & 2083 \\  & Medium Up & 1126 & 282 & 603 & 2011 \\  & Large & 1121 & 280 & 601 & 2002 \\  & **Total** & 4473 & 1118 & 2397 & 7988 \\ \hline \multirow{4}{*}{Sagittal} & Small & 854 & 213 & 458 & 1525 \\  & Medium Down & 849 & 213 & 455 & 1517 \\ \cline{1-1}  & Medium Up & 867 & 217 & 464 & 1548 \\ \cline{1-1}  & Large & 857 & 214 & 459 & 1530 \\ \cline{1-1}  & **Total** & 3427 & 857 & 1836 & 6120 \\ \hline \end{tabular}
\end{table}
Table 2: Number of samples for training, validation, and testing using ISLES 2022 in different planes.  
AdamW optimizer is \(1\times 10^{-4}\); the training phase was carried out in 200 epochs using a batch size of 16. When 30 consecutive epochs occur without improvement in the loss function, the scheduler to the optimizer decreased the learning rate to 1% of the original rate.  
The key hyperparameters of the proposed model consist of the assigned weights to the GDL and FL functions in Equation (6): \(\lambda_{GDL}\) and \(\lambda_{FL}\), correspondingly. Their selection was performed based on the F1-Scores resulting from experiments varying their values in a complementary manner and \(\gamma=2\) in Equation (4). Since the features of the ISLES 2015 and ISLES 2022 datasets differ, this process was conducted separately for each dataset. Figure 3 illustrates the results for such experiments. It can be observed that the maximum scores are obtained while using \(\lambda_{GDL}=0.3\) and \(\lambda_{FL}=0.7\) for the ISLES 2015 dataset, and \(\lambda_{GDL}=0.1\) with \(\lambda_{FL}=0.9\) for the ISLES 2022 images.