## 6 Results and Discussion  
The results of the performed experiments can be summarized from several points of view. First, the Algorithm selection has experimentally demonstrated that it can improve the accuracy when trained on well-behaved Algorithms. Well-behaved Algorithms are such that the difference between the selection confidence and the result of processing result tends to 0. This Algorithmic good behavior does, however, go against the idea of improvement by  
Figure 12: Algorithm selection results obtained by excluding some of the component Algorithms. generalization by modeling processing noise. The reason is because, in order to effectively select Algorithms for the classification task, the prediction must take into account the outliers of each Algorithm. In general, such outliers are not directly predictable, as they are outside of the main distribution of the Algorithm's input-output mapping. Therefore, training an Algorithm selection on well-behaved Algorithms can result in selecting Algorithms in such a way that does not allow for proper selection for the sample outliers. Both of these effects can be seen on Figure 10b. The best Algorithm selection accuracy is obtained at epochs 24, 28, and 30, pointing towards the fact that the Algorithm selector improves the predictive accuracy with increasing epochs of training of the component Algorithms. However, at the same time, for epochs 14, 16, and 22, the Algorithm selection does not provide any improvement when compared to the most accurate Algorithm. This would indicate that the ability to accurately select an Algorithm would benefit from directly predicting each Algorithm's outliers. Regarding the component Algorithms' training duration, while five epochs might seem limited for deep learning models, our results demonstrate this was sufficient for our experimental purposes. As seen in Figure 8, these pre-trained models achieved reasonably stable performance within this timeframe on our medical imaging dataset. In fact, the early training epochs created opportunities for complementary performance patterns that were valuable for Algorithm selection. As models train longer and approach convergence, they tend to find similar solutions, potentially reducing the benefits of Algorithm selection. Our approach intentionally balanced adequate individual performance with maintaining Algorithmic diversity, which aligns with our research focus on Algorithm selection rather than maximizing individual Algorithm accuracy. It is worth noting that our component Algorithms were deliberately chosen to represent a diverse range of architectures commonly used in medical image classification. While more advanced state-of-the-art architectures might achieve higher absolute accuracy, our focus was on demonstrating the efficacy of Algorithm selection using synthetic data rather than maximizing classification performance per se. The moderate accuracy levels (<95%) of our component Algorithms actually provide a realistic scenario where Algorithm selection can make meaningful improvements. In settings where all Algorithms achieve near-perfect accuracy, the potential benefits of selection would be minimal. Future work could explore how Algorithm selection using synthetic data performs with more advanced architectures like Vision Transformers [55], MedicalNet [56], or other domain-specific models, though we expect the core findings about synthetic data's utility for Algorithm selection to remain valid regardless of the specific architectures employed. Second, Algorithm selection experimentally benefits from synthetic data. In particular, when using the synthetic images generated from certain number of seed samples, the SAS outperforms RAS and most of the experimental cases.