A deep learning interpretable classifier for diabetic retinopathy disease grading.

• Derivation of an alternative method for the interpretation of deep learning models. • Design a human-expert level deep learning diabetic retinopathy classification model. • Design a diabetic retinopathy interpretable classifier. • Visualization of typical feature and input space score map samples. In this paper we present a diabetic retinopathy deep learning interpretable classifier. On one hand, it classifies retina images into different levels of severity with good performance. On the other hand, this classifier is able of explaining the classification results by assigning a score for each point in the hidden and input spaces. These scores indicate the pixel contribution to the final classification. To obtain these scores, we propose a new pixel-wise score propagation model that for every neuron, divides the observed output score into two components. With this method, the generated visual maps can be easily interpreted by an ophthalmologist in order to find the underlying statistical regularities that help to the diagnosis of this eye disease. [ABSTRACT FROM AUTHOR]