Title: A Deep Learning Interpretable Classifier for Diabetic Retinopathy Disease Grading,
Abstract: Deep neural network models have been proven to be very successful in image
classification tasks, also for medical diagnosis, but their main concern is its
lack of interpretability. They use to work as intuition machines with high
statistical confidence but unable to give interpretable explanations about the
reported results. The vast amount of parameters of these models make difficult
to infer a rationale interpretation from them. In this paper we present a
diabetic retinopathy interpretable classifier able to classify retine images
into the different levels of disease severity and of explaining its results by
assigning a score for every point in the hidden and input space, evaluating its
contribution to the final classification in a linear way. The generated visual
maps can be interpreted by an expert in order to compare its own knowledge with
the interpretation given by the model.