A New Approach for Explainable Multiple Organ Annotation with Few Data

TL;DR

This paper presents a novel reasoning framework that uses fuzzy relations to perform organ annotation in medical images with very limited data, providing both accurate results and explanations.

Contribution

It introduces a fuzzy relation-based reasoning approach for organ annotation that works effectively with small datasets and generates explanations.

Findings

Effective on small datasets with few examples

Provides interpretable explanations for annotations

Achieves promising accuracy in medical image organ annotation

Abstract

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.