Reliability analysis framework for computer-assisted medical decision systems.
Reliability analysis framework for computer-assisted medical decision systems.
复制标题
计算机辅助医疗决策系统的可靠性分析框架。
DOI：
10.1118/1.2432409
复制
发表时间：
2007
期刊：
影响因子：
3.8
通讯作者：
Tourassi,GeorgiaD
中科院分区：
文献类型：
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作者：
Habas,PiotrA;Zurada,JacekM;Elmaghraby,AdelS;Tourassi,GeorgiaD
我们提出了一种增强计算机辅助决策（CAD）系统的技术，使其能够评估每个决策的可靠性。可靠性评估是通过测量一个CAD系统的准确性与已知的情况下类似的问题。所提出的技术分析查询案例的特征空间邻域，以动态地选择与查询相关的已知案例的输入依赖集。此集合用于评估CAD系统的局部（查询特定）准确性。估计的局部准确性用作CAD对查询案例响应的可靠性度量。这项研究的基本假设是，CAD决策的可靠性更高，更准确。使用包含1337个感兴趣区域（ROI）的乳腺X线摄影数据库（681个肿块，656个正常实质）对上述假设进行了检验。开发了三种类型的决策模型，即反向传播神经网络（BPNN）、广义回归神经网络（GRNN）和支持向量机（SVM），以基于从每个ROI自动提取的八个形态特征来检测肿块。使用受试者工作特征（ROC）分析评价所有决策模型的性能。研究表明，所提出的可靠性措施是CAD系统的情况下特定的准确性的强预测。具体而言，高可靠性CAD预测的ROC面积指数明显优于低可靠性值的预测。这一结果在研究中调查的所有决策模型中是一致的。当提供不太可能可靠的意见时，拟议的特定案例可靠性分析技术可用于提醒CAD用户。该技术可以很容易地部署在临床环境中，因为它适用于各种分类器，无论其结构如何，并且它既不需要额外的训练，也不需要构建多个决策模型来评估特定病例的CAD准确性。
We present a technique that enhances computer‐assisted decision (CAD) systems with the ability to assess the reliability of each individual decision they make. Reliability assessment is achieved by measuring the accuracy of a CAD system with known cases similar to the one in question. The proposed technique analyzes the feature space neighborhood of the query case to dynamically select an input‐dependent set of known cases relevant to the query. This set is used to assess the local (query‐specific) accuracy of the CAD system. The estimated local accuracy is utilized as a reliability measure of the CAD response to the query case. The underlying hypothesis of the study is that CAD decisions with higher reliability are more accurate. The above hypothesis was tested using a mammographic database of 1337 regions of interest (ROIs) with biopsy‐proven ground truth (681 with masses, 656 with normal parenchyma). Three types of decision models, a back‐propagation neural network (BPNN), a generalized regression neural network (GRNN), and a support vector machine (SVM), were developed to detect masses based on eight morphological features automatically extracted from each ROI. The performance of all decision models was evaluated using the Receiver Operating Characteristic (ROC) analysis. The study showed that the proposed reliability measure is a strong predictor of the CAD system's case‐specific accuracy. Specifically, the ROC area index for CAD predictions with high reliability was significantly better than for those with low reliability values. This result was consistent across all decision models investigated in the study. The proposed case‐specific reliability analysis technique could be used to alert the CAD user when an opinion that is unlikely to be reliable is offered. The technique can be easily deployed in the clinical environment because it is applicable with a wide range of classifiers regardless of their structure and it requires neither additional training nor building multiple decision models to assess the case‐specific CAD accuracy.