Next, the first order statistics of the no. 36 kernel and the second order statistics of the no. 28 kernel were calculated; these refer to the designs of the no. 13,751 and no. 13,768 features. The characteristics of the filter response were reflected in the two parameters. As the second order statistics of the no. 28 kernel show, the no. 107 filter response of the wild-type tumor was more internally complicated and had more texture information. As the first order statistics of the no. 36 kernel show, the no. 107 filter response of the mutation-bearing tumors had lower intensity and was more gathered around the no. 36 kernel (the no. 36 kernel had a mean value of −0.1352 and a variance of 0.0055). Thus, the prediction based on CNN features clearly provided good results. Indeed, the two types of tumors have significantly different responses in deep filter banks. 解释

这段文字进一步解释了第107个深度滤波器的响应特征。通过计算第36个内核的一阶统计量和第28个内核的二阶统计量，得到了特征13,751和13,768的设计。这两个特征中反映了滤波器响应的特征。通过第28个内核的二阶统计量可以看出，野生型肿瘤的第107个滤波器响应更加内部复杂，具有更多的纹理信息。而通过第36个内核的一阶统计量可以看出，携带IDH1基因突变的肿瘤的第107个滤波器响应强度较低，并且更集中在第36个内核周围（第36个内核的平均值为-0.1352，方差为0.0055）。因此，基于CNN特征的预测提供了良好的结果。实际上，这两种肿瘤在深度滤波器组中的响应有显著的差异。

相关问题

Then, the median absolute deviations (MAD) was calculated for each remained feature21. Features with MAD equal to zero were discarded, as these features were considered as non-informative. After this step, 33881 features were left. Next, we further selected features with prognostic value. Here the prognostic performance is assessed using the concordance index (C-index), a generalization of the area under the receiver operating characteristic (ROC) curve (AUC)22. The C-index for each feature was calculated. Features with C-index ≥ 0.580 are considered as predictive factors. After prognostic performance analysis, 1581 features remained. Then, we further reduced the data dimension by removing highly correlated features. Here the correlation coefficient between each pair of features is calculated. For feature pair with correlated coefficient ≥0.90, the more prognostic feature is retained and the other feature is removed. Finally, the remained 150 image features are selected and regarded as robust, predictive and nonredundant. 解释

该段文字描述了一个数据特征选择的过程。首先，对于所有特征，计算其中位数绝对偏差（MAD），并移除MAD等于零的特征，因为这些特征被认为是非信息性的。经过此步骤，剩下33881个特征。然后，使用协调指数（C-index）对这些特征进行预测价值分析，C-index是接收者操作特征（ROC）曲线下面积（AUC）的推广。具有C-index≥0.580的特征被视为预测性因素。经过预测性能分析后，剩下1581个特征。接下来，通过计算特征之间的相关系数，进一步减少数据维度。对于相关系数≥0.90的特征对，保留更具预测性的特征，移除另一个特征。最后，剩下150个图像特征被选为具有稳健性、预测性和非冗余性的特征。

用中文解释This is a MATLAB function that takes in a confusion matrix and a boolean variable called 'verbatim'. The confusion matrix is a 3x3 matrix that is the output of the MATLAB function 'confusionmat'. The function then computes various statistics based on this confusion matrix and returns them in a struct called 'stats'. The statistics computed are true positives (tp), false positives (fp), false negatives (fn), and true negatives (tn). These statistics are calculated for each class in the confusion matrix. The function loops through each class and computes the corresponding statistics. The 'verbose' variable controls whether the generated table is output to the command window. If 'verbose' is set to 1, the table is output. Otherwise, it is not. Overall, this function is useful for evaluating the performance of a classification algorithm by analyzing the confusion matrix.

这是一个MATLAB函数，它接受一个混淆矩阵和一个名为'verbose'的布尔变量作为输入。混淆矩阵是一个3x3矩阵，是MATLAB函数'confusionmat'的输出。该函数基于混淆矩阵计算各种统计数据，并将它们返回到名为'stats'的结构体中。计算的统计数据包括真正例(tp)、假正例(fp)、假负例(fn)和真负例(tn)。这些统计数据是针对混淆矩阵中的每个类别计算的。函数通过循环遍历每个类别并计算相应的统计数据。'verbose'变量控制生成的表格是否输出到命令窗口。如果'verbose'设置为1，则输出表格，否则不输出。总的来说，这个函数通过分析混淆矩阵，有助于评估分类算法的性能。