area under the curve (AUC)有什么意义

？AUC是一种用于评估分类模型性能的指标，它表示ROC曲线下的面积。AUC的值越大，说明模型的性能越好，因为ROC曲线下的面积越大，说明模型在不同阈值下的分类效果都比较好。因此，AUC可以用来比较不同分类模型的性能，也可以用来选择最佳的分类阈值。

相关问题

Area Under the Curve(AUC)概念

Area Under the Curve (AUC)是一种用于衡量分类器性能的指标。在ROC曲线中，AUC表示ROC曲线下方的面积。ROC曲线是以False Positive Rate（FPR）为横坐标，True Positive Rate（TPR）为纵坐标绘制的曲线，用于表示分类器在不同阈值下的性能表现。AUC的取值范围在0到1之间，AUC越大表示分类器的性能越好。当AUC等于1时，表示分类器完美地对样本进行了分类，当AUC等于0.5时，表示分类器的性能和随机猜测相同，当AUC小于0.5时，表示分类器的性能比随机猜测还要差。

Deep learning-based radiomics (DLR) was developed to extract deep information from multiple modalities of magnetic resonance (MR) images. The performance of DLR for predicting the mutation status of isocitrate dehydrogenase 1 (IDH1) was validated in a dataset of 151 patients with low-grade glioma. A modified convolutional neural network (CNN) structure with 6 convolutional layers and a fully connected layer with 4096 neurons was used to segment tumors. Instead of calculating image features from segmented images, as typically performed for normal radiomics approaches, image features were obtained by normalizing the information of the last convolutional layers of the CNN. Fisher vector was used to encode the CNN features from image slices of different sizes. High-throughput features with dimensionality greater than 1.6*104 were obtained from the CNN. Paired t-tests and F-scores were used to select CNN features that were able to discriminate IDH1. With the same dataset, the area under the operating characteristic curve (AUC) of the normal radiomics method was 86% for IDH1 estimation, whereas for DLR the AUC was 92%. The AUC of IDH1 estimation was further improved to 95% using DLR based on multiple-modality MR images. DLR could be a powerful way to extract deep information from medical images. 解释

这段文本介绍了一种基于深度学习的影像分析方法，即Deep learning-based radiomics (DLR)。该方法使用了修改后的卷积神经网络(CNN)结构，通过对磁共振(MR)影像进行多模态分析，提取更深层次的信息。该研究使用了151例低级别胶质瘤患者的数据集，通过对肿瘤进行分割，得到CNN的最后卷积层信息，再利用Fisher向量进行编码，得到维度高达1.6*104的高通量特征。该研究表明，相比于传统的放射学方法，DLR方法在预测异柠酸脱氢酶1(IDH1)突变状态时，具有更高的准确性，其AUC值达到了92%~95%。这些发现表明，DLR方法可以有效地从医学影像中提取更深层次的信息，对肿瘤的诊断和治疗具有潜在的帮助。