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首页> 外文期刊>Medical Physics >Analysis and minimization of overtraining effect in rule-based classifiers for computer-aided diagnosis.
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Analysis and minimization of overtraining effect in rule-based classifiers for computer-aided diagnosis.

机译：分析和最小化基于规则的计算机辅助诊断分类器中的过度训练效果。

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摘要

Computer-aided diagnostic (CAD) schemes have been developed to assist radiologists detect various lesions in medical images. In CAD schemes, classifiers play a key role in achieving a high lesion detection rate and a low false-positive rate. Although many popular classifiers such as linear discriminant analysis and artificial neural networks have been employed in CAD schemes for reduction of false positives, a rule-based classifier has probably been the simplest and most frequently used one since the early days of development of various CAD schemes. However, with existing rule-based classifiers, there are major disadvantages that significantly reduce their practicality and credibility. The disadvantages include manual design, poor reproducibility, poor evaluation methods such as resubstitution, and a large overtraining effect. An automated rule-based classifier with a minimized overtraining effect can overcome or significantly reduce the extent of the above-mentioned disadvantages. In this study, we developed an "optimal" method for the selection of cutoff thresholds and a fully automated rule-based classifier. Experimental results performed with Monte Carlo simulation and a real lung nodule CT data set demonstrated that the automated threshold selection method can completely eliminate overtraining effect in the procedure of cutoff threshold selection, and thus can minimize overall overtraining effect in the constructed rule-based classifier. We believe that this threshold selection method is very useful in the construction of automated rule-based classifiers with minimized overtraining effect.
机译：已经开发了计算机辅助诊断（CAD）方案，以帮助放射科医生检测医学图像中的各种病变。在CAD方案中，分类器在实现高病变检测率和低假阳性率中起关键作用。尽管在CAD方案中使用了许多流行的分类器（例如线性判别分析和人工神经网络）来减少误报，但是自从各种CAD方案发展以来，基于规则的分类器可能是最简单，最常用的分类器。但是，对于现有的基于规则的分类器，存在主要缺点，这些缺点会大大降低其实用性和可信度。缺点包括手动设计，可重复性差，评估方法（如替换）差，过度训练效果大。具有最小化过度训练效果的基于规则的自动分类器可以克服或显着减少上述缺点的程度。在这项研究中，我们开发了一种用于选择截止阈值的“最佳”方法和一个基于规则的全自动分类器。通过蒙特卡罗模拟和真实肺结节CT数据集进行的实验结果表明，自动阈值选择方法可以完全消除阈值阈值选择过程中的过度训练效果，从而可以在构造的基于规则的分类器中最大程度地降低总体过度训练效果。我们认为，此阈值选择方法在构建自动的基于规则的分类器时具有非常有用的作用，该分类器具有最小的过度训练效果。

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