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首页> 外文会议>PRICAI'98 : Topics in artificial intelligence >Learning First-Order Rules from Image Applied to Glaucoma Diagnosis
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Learning First-Order Rules from Image Applied to Glaucoma Diagnosis

机译：从图像中学习一阶规则应用于青光眼诊断

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Computer-based diagnosis from image data is important for medicine. In particular, for the glaucoma diagnosis we target here, the ocular fundus image can be easily obtained and can be used to automatically identify whether an eye is glaucomatous or not. However, the image has a two-dimensional distribution, and it is difficult to feature the whole image through some real-valued parameters in general. This paper proposes a machine learning method using a set of expert's decision cases that identify local abnormalities of an image. This method finds regularities between an image set and the decision cases using Inductive Logic Programming (ILP). Unlike decision-tree learning and neural networks, ILP allows relational learning between concepts. Learned rules are abstract enough to absorb noisy data obtained directly from image analysis. We applied the method to detecting early glaucomatous eyes. Our ILP system, GKS produced 30 rules from 2000 positive and negative examples that were obtained by segmenting 39 glaucomatous eyes. A 10-fold cross validation assessment shows about 80% sensitivity and 65% accuracy of the rules, resulting in the high performance comparable with human-level classification.
机译：基于图像数据的基于计算机的诊断对医学很重要。特别是，对于我们此处针对的青光眼诊断，可以轻松获得眼底图像，并可将其用于自动识别眼睛是否为青光眼。但是，图像具有二维分布，通常很难通过一些实值参数对整个图像进行特征描述。本文提出了一种使用一组专家决策案例的机器学习方法，这些案例可以识别图像的局部异常。该方法使用归纳逻辑编程（ILP）在图像集和决策案例之间找到规律性。与决策树学习和神经网络不同，ILP允许概念之间的关系学习。学习到的规则足够抽象，可以吸收直接从图像分析获得的嘈杂数据。我们将该方法用于检测早期青光眼。我们的ILP系统GKS通过对39例青光眼的眼睛进行分割，从2000个阳性和阴性实例中产生了30条规则。 10倍交叉验证评估显示出规则的大约80％的敏感性和65％的准确性，从而产生了与人类级别分类相当的高性能。

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