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.

Submitted to Elsevier

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Cited by in corpus (5)

• DR$|$GRADUATE: uncertainty-aware deep learning-based diabetic retinopathy grading in eye fundus images
• Deep Weakly-Supervised Learning Methods for Classification and Localization in Histology Images: A Survey
• Prototype-based interpretation of the functionality of neurons in winner-take-all neural networks
• Applications of Deep Learning in Fundus Images: A Review
• Identification and Visualization of the Underlying Independent Causes of the Diagnostic of Diabetic Retinopathy made by a Deep Learning Classifier