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A New Approach for Explainable Multiple Organ Annotation with Few Data

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.
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Submitted on : Monday, December 30, 2019 - 2:36:08 PM
Last modification on : Saturday, February 19, 2022 - 3:13:46 AM
Long-term archiving on: : Tuesday, March 31, 2020 - 1:41:10 PM

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  • HAL Id : hal-02418480, version 1
  • ARXIV : 1912.12932

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Régis Pierrard, Jean-Philippe Poli, Céline Hudelot. A New Approach for Explainable Multiple Organ Annotation with Few Data. IJCAI 2019 Workshop on Explainable Artificial Intelligence (XAI), Aug 2019, Macao, Macau SAR China. ⟨hal-02418480⟩

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