  Despite the recent successes of deep learning, such models are still far from
some human abilities like learning from few examples, reasoning and explaining
decisions. In this paper, we focus on organ annotation in medical images and we
introduce a reasoning framework that is based on learning fuzzy relations on a
small dataset for generating explanations. Given a catalogue of relations, it
efficiently induces the most relevant relations and combines them for building
constraints in order to both solve the organ annotation task and generate
explanations. We test our approach on a publicly available dataset of medical
images where several organs are already segmented. A demonstration of our model
is proposed with an example of explained annotations. It was trained on a small
training set containing as few as a couple of examples.
