Enhancing Few-Shot Chest X-ray Classification through Generative Class Augmentation.

Meta-learning-based models trained on multiple classification tasks based on multiple classes can adapt to new classification tasks with limited training samples, thereby achieving few-shot learning. However, when the number of classes in the classification task chest X-ray image analysis is also limited, meta-learning can result in overfitting. This study sought to overcome this with a class augmentation method using a generative adversarial network to generate pseudo-classes, thereby increasing the number of classes.