Title: Case Study: Explaining Diabetic Retinopathy Detection Deep CNNs via Integrated Gradients,
Abstract: In this report, we applied integrated gradients to explaining a neural
network for diabetic retinopathy detection. The integrated gradient is an
attribution method which measures the contributions of input to the quantity of
interest. We explored some new ways for applying this method such as explaining
intermediate layers, filtering out unimportant units by their attribution value
and generating contrary samples. Moreover, the visualization results extend the
use of diabetic retinopathy detection model from merely predicting to assisting
finding potential lesions.