论文标题
基于神经符号学习的可解释和可解释的糖尿病性视网膜病变分类
Explainable and Interpretable Diabetic Retinopathy Classification Based on Neural-Symbolic Learning
论文作者
论文摘要
在本文中，我们提出了一种基于神经符号学习的可解释和可解释的糖尿病性视网膜病变（Instrydr）分类模型。为了获得解释性，在决策中应考虑高级符号表示。具体而言，我们引入了人类可读的符号表示，该表示遵循与眼睛健康状况有关的糖尿病性视网膜病特征的分类学风格，以实现可解释性。然后，我们在疾病预测中包括从符号表示获得的人类可读特征。糖尿病性视网膜病变分类数据集的实验结果表明，与应用于IDRID数据集的最新方法相比，我们提出的解释性方法表现出有希望的性能，同时还提供了可解释性和解释性。
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.
