Anastasio M A, Kupinski M A, Nishikawa R M
IEEE Trans Med Imaging. 1998 Dec;17(6):1089-93. doi: 10.1109/42.746726.
Computerized detection schemes have the potential of increasing diagnostic accuracy in medical imaging by alerting radiologists to lesions that they initially overlooked. These schemes typically employ multiple parameters such as threshold values or filter weights to arrive at a detection decision. In order for the system to have high performance, the values of these parameters need to be set optimally. Conventional optimization techniques are designed to optimize a scalar objective function. The task of optimizing the performance of a computerized detection scheme, however, is clearly a multiobjective problem: we wish to simultaneously improve the sensitivity and false-positive rate of the system. In this work we investigate a multiobjective approach to optimizing computerized rule-based detection schemes. In a multiobjective optimization, multiple objectives are simultaneously optimized, with the objective now being a vector-valued function. The multiobjective optimization problem admits a set of solutions, known as the Pareto-optimal set, which are equivalent in the absence of any information regarding the preferences of the objectives. The performances of the Pareto-optimal solutions can be interpreted as operating points on an optimal free-response receiver operating characteristic (FROC) curve, greater than or equal to the points on any possible FROC curve for a given dataset and detection scheme. It is demonstrated that generating FROC curves in this manner eliminates several known problems with conventional FROC curve generation techniques for rule-based detection schemes. We employ the multiobjective approach to optimize a rule-based scheme for clustered microcalcification detection that has been developed in our laboratory.
计算机化检测方案有可能通过提醒放射科医生注意他们最初忽略的病变来提高医学成像中的诊断准确性。这些方案通常采用多个参数，如阈值或滤波器权重，来做出检测决策。为了使系统具有高性能，这些参数的值需要进行优化设置。传统的优化技术旨在优化一个标量目标函数。然而，优化计算机化检测方案性能的任务显然是一个多目标问题：我们希望同时提高系统的灵敏度和假阳性率。在这项工作中，我们研究了一种多目标方法来优化基于规则的计算机化检测方案。在多目标优化中，多个目标同时进行优化，此时目标是一个向量值函数。多目标优化问题允许有一组解，称为帕累托最优集，在没有任何关于目标偏好信息的情况下，这些解是等效的。帕累托最优解的性能可以解释为最优自由响应接收器操作特性（FROC）曲线上的工作点，对于给定的数据集和检测方案，这些点大于或等于任何可能的FROC曲线上的点。结果表明，以这种方式生成FROC曲线消除了基于规则的检测方案中传统FROC曲线生成技术的几个已知问题。我们采用多目标方法来优化我们实验室开发的基于规则的簇状微钙化检测方案。
