Identification and Visualization of the Underlying Independent Causes of the Diagnostic of Diabetic Retinopathy made by a Deep Learning Classifier
This addresses the need for interpretability in AI-driven medical diagnostics, specifically for diabetic retinopathy classification, though it appears incremental as it builds on existing interpretability techniques.
The paper tackled the problem of interpreting deep learning classifiers for medical diagnosis by identifying independent causes used by the model to classify diabetic retinopathy from eye fundus images, concluding that only 3 independent components are sufficient for correct classification across 5 disease levels.
Interpretability is a key factor in the design of automatic classifiers for medical diagnosis. Deep learning models have been proven to be a very effective classification algorithm when trained in a supervised way with enough data. The main concern is the difficulty of inferring rationale interpretations from them. Different attempts have been done in last years in order to convert deep learning classifiers from high confidence statistical black box machines into self-explanatory models. In this paper we go forward into the generation of explanations by identifying the independent causes that use a deep learning model for classifying an image into a certain class. We use a combination of Independent Component Analysis with a Score Visualization technique. In this paper we study the medical problem of classifying an eye fundus image into 5 levels of Diabetic Retinopathy. We conclude that only 3 independent components are enough for the differentiation and correct classification between the 5 disease standard classes. We propose a method for visualizing them and detecting lesions from the generated visual maps.