  Deep learning is quickly becoming the leading methodology for medical image
analysis. Given a large medical archive, where each image is associated with a
diagnosis, efficient pathology detectors or classifiers can be trained with
virtually no expert knowledge about the target pathologies. However, deep
learning algorithms, including the popular ConvNets, are black boxes: little is
known about the local patterns analyzed by ConvNets to make a decision at the
image level. A solution is proposed in this paper to create heatmaps showing
which pixels in images play a role in the image-level predictions. In other
words, a ConvNet trained for image-level classification can be used to detect
lesions as well. A generalization of the backpropagation method is proposed in
order to train ConvNets that produce high-quality heatmaps. The proposed
solution is applied to diabetic retinopathy (DR) screening in a dataset of
almost 90,000 fundus photographs from the 2015 Kaggle Diabetic Retinopathy
competition and a private dataset of almost 110,000 photographs (e-ophtha). For
the task of detecting referable DR, very good detection performance was
achieved: $A_z = 0.954$ in Kaggle's dataset and $A_z = 0.949$ in e-ophtha.
Performance was also evaluated at the image level and at the lesion level in
the DiaretDB1 dataset, where four types of lesions are manually segmented:
microaneurysms, hemorrhages, exudates and cotton-wool spots. The proposed
detector outperforms recent algorithms trained to detect those lesions
specifically, as well as competing heatmap generation algorithms for ConvNets.
This detector is part of the Messidor system for mobile eye pathology
screening. Because it does not rely on expert knowledge or manual segmentation
for detecting relevant patterns, the proposed solution is a promising image
mining tool, which has the potential to discover new biomarkers in images.
