Interpretable Medical Image Classification using Prototype Learning and
  Privileged Information
Luisa Gallee Meinrad Beer Michael Goetz
Interpretability is often an essential requirement in medical imaging.
Advanced deep learning methods are required to address this need for
explainability and high performance. In this work, we investigate whether
additional information available during the training process can be used to
create an understandable and powerful model. We propose an innovative solution
called Proto-Caps that leverages the benefits of capsule networks, prototype
learning and the use of privileged information. Evaluating the proposed
solution on the LIDC-IDRI dataset shows that it combines increased
interpretability with above state-of-the-art prediction performance. Compared
to the explainable baseline model, our method achieves more than 6 % higher
accuracy in predicting both malignancy (93.0 %) and mean characteristic
features of lung nodules. Simultaneously, the model provides case-based
reasoning with prototype representations that allow visual validation of
radiologist-defined attributes.