StackMaps: A Visualization Technique for Diabetic Retinopathy Grading

Convolution Neural Networks (CNN) excelled humans in many classification tasks, including medical imaging applications. However, model interpretation is still an active research area. In this paper, we address the model interpretation problem for the Diabetic Retinopathy grading task. We propose a novel visualization method called StackMaps. Our proposed method is class-agnostic, which fits the diabetic retinopathy grading problem better than other alternatives. Moreover, unlike previous visualization methods, StackMaps gets rid of the dependency on gradients. Our StackMaps technique gets the most significant feature maps at the last convolutional layer after one forward pass. We can also get the significant feature maps from lower layers using beam search guided by the feature maps obtained from the previous layer. Finally, we compute the final map as the sum of the significant feature maps obtained at each layer. We evaluate StackMaps against other state-of-the-art visualization methods qualitatively and quantitatively. We used the FGADR dataset to define our experimental setup. We show that StackMaps achieves better visual interpretation and lesion localization.