  A key component to the success of deep learning is the availability of
massive amounts of training data. Building and annotating large datasets for
solving medical image classification problems is today a bottleneck for many
applications. Recently, capsule networks were proposed to deal with
shortcomings of Convolutional Neural Networks (ConvNets). In this work, we
compare the behavior of capsule networks against ConvNets under typical
datasets constraints of medical image analysis, namely, small amounts of
annotated data and class-imbalance. We evaluate our experiments on MNIST,
Fashion-MNIST and medical (histological and retina images) publicly available
datasets. Our results suggest that capsule networks can be trained with less
amount of data for the same or better performance and are more robust to an
imbalanced class distribution, which makes our approach very promising for the
medical imaging community.
