Estimating Neural Network Precision for Cancer Diagnosis

描述（由申请人提供）：本申请广泛、长期 目的是准确诊断癌症，减少假阴性和 假阳性诊断本申请中提出的研究涉及 人工神经网络（ANN）是重要的统计工具， 经常用于计算机辅助诊断（CAD）方法， 改善癌症诊断。这项研究的目的是提供误差线 ANN输出。这项研究的意义和健康相关性是 这个目标，如果实现，可以代表一个根本性的进步，人工神经网络， CAD应用程序，使ANN输出更可靠的后续计算机 处理和更直观地理解放射科医生解释， 将（人工神经网络的诊断预测）纳入自己的诊断中。 所提出的方法可能成为实践标准，取代 传统的一个ANN方法。有待检验的假设是， 神经网络输出具有有限的不确定性，可以估计， 以置信区间表示。具体目标是： (1)为了定性地证明ANN输出中的不确定性概念， 从一组训练中开发多个ANN的可行性 例 (2)开发和验证人工神经网络精度估计的定量方法。 (3)将ANN精度估计应用于现实世界的CAD问题：计算机 恶性和良性簇状微钙化的分类。 研究设计主要是使用计算机模拟来调查 从同一训练中获得的多个ANN的输出的性质 案例，并制定和验证计算置信度的实用方法 从这些多个人工神经网络的人工神经网络输出的时间间隔，然后应用新的方法 一个真实的CAD任务。采用的方法包括计算机模拟， ROC分析，置信区间计算，自举，参数 统计分布估计、分析和计算机 分析乳房X线照片中的乳房病变。

DESCRIPTION (Provided by Applicant): This application's broad, long-term objective is to diagnose cancer accurately, reducing both false-negative and false-positive diagnoses. The research proposed in this application concerns artificial neural networks (ANNs) which are important statistical tools that are used frequently in computer-aided diagnosis (CAD) methods intended to improve cancer diagnosis. The goal of this research is to provide error bars for ANN output. The significance and health-relatedness of this research is that the goal, if achieved, could represent a fundamental advance to ANNs in CAD applications, by making ANN output more reliable for subsequent computer processing and more intuitive to understand for radiologists to interpret and incorporate (the diagnostic predictions made by ANNs) in their own diagnoses. The proposed method could become the standard of practice, replacing the conventional, one-ANN approach. The hypothesis to be tested is that artificial neural network output has finite uncertainty which can be estimated and expressed in terms of confidence intervals. The specific aims are: (1) To demonstrate qualitatively the concept of uncertainty in ANN output and the feasibility of developing multiple ANNs from a single set of training cases. (2) To develop and validate quantitative method(s) of ANN-precision estimation. (3) To apply ANN-precision estimation to a real-world CAD problem: computer classification of malignant and benign clustered microcalcifications. The research design is to use primarily computer simulations to investigate properties of the output from multiple ANNs obtained from the same training cases and to develop and validate practical method(s) for computing confidence intervals in ANN output from these multiple ANNs, then to apply the new methods to a real-world CAD task. The methods to be used include computer simulation, ROC analysis, computation of confidence intervals, bootstrapping, parametric estimation of statistical distributions, analytical analysis, and computer analysis of breast lesions in mammograms.