论文标题
经过修改的AUC培训卷积神经网络：考虑到信心
A Modified AUC for Training Convolutional Neural Networks: Taking Confidence into Account
论文作者
论文摘要
接收器操作特性（ROC）曲线是二进制分类的一项信息，而ROC曲线下的区域（AUC）是报告二进制分类器的性能的流行指标。在本文中，首先，我们对ROC曲线和AUC指标进行了全面的综述。接下来，我们提出了一个修改的AUC版本，该版本将模型信心考虑在内，同时将AUC纳入用于训练卷积神经网络进行分类任务的二进制交叉熵（BCE）损失中。我们在三个数据集上证明了这一点：MNIST，前列腺MRI和Brain MRI。此外，我们发表了一个新的Python库Gureineai，它为常规AUC和提议的修改AUC提供了功能以及ROC曲线每个点的灵敏度，特异性，回忆，精度和F1在内的指标。
Receiver operating characteristic (ROC) curve is an informative tool in binary classification and Area Under ROC Curve (AUC) is a popular metric for reporting performance of binary classifiers. In this paper, first we present a comprehensive review of ROC curve and AUC metric. Next, we propose a modified version of AUC that takes confidence of the model into account and at the same time, incorporates AUC into Binary Cross Entropy (BCE) loss used for training a Convolutional neural Network for classification tasks. We demonstrate this on three datasets: MNIST, prostate MRI, and brain MRI. Furthermore, we have published GenuineAI, a new python library, which provides the functions for conventional AUC and the proposed modified AUC along with metrics including sensitivity, specificity, recall, precision, and F1 for each point of the ROC curve.
