  In recent years, Artificial Intelligence (AI) has proven its relevance for
medical decision support. However, the "black-box" nature of successful AI
algorithms still holds back their wide-spread deployment. In this paper, we
describe an eXplanatory Artificial Intelligence (XAI) that reaches the same
level of performance as black-box AI, for the task of classifying Diabetic
Retinopathy (DR) severity using Color Fundus Photography (CFP). This algorithm,
called ExplAIn, learns to segment and categorize lesions in images; the final
image-level classification directly derives from these multivariate lesion
segmentations. The novelty of this explanatory framework is that it is trained
from end to end, with image supervision only, just like black-box AI
algorithms: the concepts of lesions and lesion categories emerge by themselves.
For improved lesion localization, foreground/background separation is trained
through self-supervision, in such a way that occluding foreground pixels
transforms the input image into a healthy-looking image. The advantage of such
an architecture is that automatic diagnoses can be explained simply by an image
and/or a few sentences. ExplAIn is evaluated at the image level and at the
pixel level on various CFP image datasets. We expect this new framework, which
jointly offers high classification performance and explainability, to
facilitate AI deployment.
