MetaMed: Few-shot medical image classification using gradient-based meta-learning

•Efficacy of a gradient-based meta-learning algorithm for few-shot learning problem on real-world non-uniformly distributed medical image datasets is analyzed.•Our experiments empirically validate that the use of meta-learning increases the confidence of predictions and robustness.•Our work proves that normal augmentation strategies fail to regularize the network in gradient-based meta-learning problems.•Hence, we integrated advanced augmentation strategies that can generate virtual samples as well as labels.•Showcasing the advantages of advanced augmentation techniques on three complex medical image datasets.•Our work significantly reduces the need to collect and annotate large data for deep learning applications in the medical domain.