  We proposed a deep learning method for interpretable diabetic retinopathy
(DR) detection. The visual-interpretable feature of the proposed method is
achieved by adding the regression activation map (RAM) after the global
averaging pooling layer of the convolutional networks (CNN). With RAM, the
proposed model can localize the discriminative regions of an retina image to
show the specific region of interest in terms of its severity level. We believe
this advantage of the proposed deep learning model is highly desired for DR
detection because in practice, users are not only interested with high
prediction performance, but also keen to understand the insights of DR
detection and why the adopted learning model works. In the experiments
conducted on a large scale of retina image dataset, we show that the proposed
CNN model can achieve high performance on DR detection compared with the
state-of-the-art while achieving the merits of providing the RAM to highlight
the salient regions of the input image.
