OBSERVER AND FEATURE ANALYSIS ON DIAGNOSIS OF RETINOPATHY OF PREMATURITY.

IEEE International Workshop on Machine Learning for Signal Processing : [proceedings]. IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2012-01-01 DOI:10.1109/MLSP.2012.6349809

E Ataer-Cansizoglu, S You, J Kalpathy-Cramer, K Keck, M F Chiang, D Erdogmus

引用次数: 10

Abstract

Retinopathy of prematurity (ROP) is a disease affecting low-birth weight infants and is a major cause of childhood blindness. However, human diagnoses is often subjective and qualitative. We propose a method to analyze the variability of expert decisions and the relationship between the expert diagnoses and features. The analysis is based on Mutual Information and Kernel Density Estimation on features. The experiments are carried out on a dataset of 34 retinal images diagnosed by 22 experts. The results show that a group of observers decide consistently with each other and there are popular features that have a high correlation with labels.

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早产儿视网膜病变诊断的观察与特征分析。

早产儿视网膜病变(ROP)是一种影响低出生体重婴儿的疾病，是儿童失明的主要原因。然而，人类的诊断往往是主观的和定性的。我们提出了一种方法来分析专家决策的可变性以及专家诊断与特征之间的关系。该分析是基于互信息和核密度估计的特征。实验是在由22位专家诊断的34张视网膜图像的数据集上进行的。结果表明，一组观察者的决定是一致的，并且存在与标签高度相关的流行特征。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE International Workshop on Machine Learning for Signal Processing : [proceedings]. IEEE International Workshop on Machine Learning for Signal Processing

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