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

Highlights：

• 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.