Explainable and Interpretable Diabetic Retinopathy Classification Based on Neural-Symbolic Learning

Autor:	Jang, Se-In, Girard, Michael J. A., Thiery, Alexandre H.
Rok vydání:	2022
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence Computer Science - Computer Vision and Pattern Recognition Computer Science - Symbolic Computation Electrical Engineering and Systems Science - Image and Video Processing
Druh dokumentu:	Working Paper
Popis:	In this paper, we propose an explainable and interpretable diabetic retinopathy (ExplainDR) classification model based on neural-symbolic learning. To gain explainability, a highlevel symbolic representation should be considered in decision making. Specifically, we introduce a human-readable symbolic representation, which follows a taxonomy style of diabetic retinopathy characteristics related to eye health conditions to achieve explainability. We then include humanreadable features obtained from the symbolic representation in the disease prediction. Experimental results on a diabetic retinopathy classification dataset show that our proposed ExplainDR method exhibits promising performance when compared to that from state-of-the-art methods applied to the IDRiD dataset, while also providing interpretability and explainability. Comment: Published in AAAI-22 Workshop
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2204.00624 Zobrazit plný text záznamu View this record from Arxiv