A New Approach for Explainable Multiple Organ Annotation with Few Data
This addresses the challenge of explainable AI in medical imaging for clinicians, but it appears incremental as it builds on existing reasoning methods for few-shot learning.
The paper tackles the problem of organ annotation in medical images with limited data by introducing a reasoning framework that learns fuzzy relations to both solve the annotation task and generate explanations, achieving results with as few as a couple of training examples.
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.