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

Abstract

The occurrence of long-tailed distributions and unavailability of high-quality annotated images is a common phenomenon in medical datasets. The use of conventional Deep Learning techniques to obtain an unbiased model with high generalization accuracy for such datasets is a challenging task. Thus, we formulated a few-shot learning problem and presented a meta-learning-based “MetaMed” approach. The model presented here can adapt to rare disease classes with the availability of few images, and less compute. MetaMed is validated on three publicly accessible medical datasets – Pap smear, BreakHis, and ISIC 2018. We used advanced image augmentation techniques like CutOut, MixUp, and CutMix to overcome the problem of over-fitting. Our approach has shown promising results on all the three datasets with an accuracy of more than 70%. Inclusion of advanced augmentation techniques regularizes the model and increases the generalization capability by 2–5%. Comparative analysis of MetaMed against transfer learning demonstrated that MetaMed classifies images with a higher confidence score and on average outperforms transfer learning for 3, 5, and 10-shot tasks for both 2-way and 3-way classification.