  Highly imbalanced datasets are ubiquitous in medical image classification
problems. In such problems, it is often the case that rare classes associated
to less prevalent diseases are severely under-represented in labeled databases,
typically resulting in poor performance of machine learning algorithms due to
overfitting in the learning process. In this paper, we propose a novel
mechanism for sampling training data based on the popular MixUp regularization
technique, which we refer to as Balanced-MixUp. In short, Balanced-MixUp
simultaneously performs regular (i.e., instance-based) and balanced (i.e.,
class-based) sampling of the training data. The resulting two sets of samples
are then mixed-up to create a more balanced training distribution from which a
neural network can effectively learn without incurring in heavily under-fitting
the minority classes. We experiment with a highly imbalanced dataset of retinal
images (55K samples, 5 classes) and a long-tail dataset of gastro-intestinal
video frames (10K images, 23 classes), using two CNNs of varying representation
capabilities. Experimental results demonstrate that applying Balanced-MixUp
outperforms other conventional sampling schemes and loss functions specifically
designed to deal with imbalanced data. Code is released at
https://github.com/agaldran/balanced_mixup .
