Synthetic Data-Based Algorithm Selection for Medical Image Classification Under Limited Data Availability

The Algorithm selection approach improves performance by dynamically choosing the optimal Algorithm for each input instance. While this selection strategy has been extensively studied, the amount of data and their nature have not yet been investigated with respect to meta-learning, particularly in scenarios with limited data availability. This paper addresses a critical challenge: where additional data might not be available for training an Algorithm selector, and to implement a selection mechanism, data must be generated. Focusing on medical image classification, we investigate whether synthetic data can effectively train an Algorithm selector when real training data are scarce. Our methodology involves data generation using Generative Adversarial Network. To determine if Algorithm selection trained on synthetically generated data can achieve the same accuracy as if trained on real-world natural data, we systematically evaluate the data generative model using the smallest amount of data needed to choose the right Algorithm and to achieve the expected level of accuracy. Our experimental results demonstrate that using a small amount of real samples can provide enough information to a Generative Adversarial Network to synthesize a new dataset that, when used for training the Algorithm selection, improves image classification in some cases.