Table 2

Validation between data sets

Feature Extraction

Data set

AUC

Sensitivity (%)

Specificity (%)

Mf

AVG

STD

AVG

STD


IR & HE

Train

0.994

0.0006

90

98.30

0.68

13

95

96.58

1.10

99

91.55

2.55


Test

0.956

0.0089

90

88.57

5.96

95

81.92

5.28

99

26.86

15.50


HE only

Train

0.986

0.0021

90

97.77

0.97

10

95

91.56

2.49

99

79.29

4.47


Test

0.918

0.0100

90

65.51

8.37

95

46.14

7.53

99

13.29

6.94


A classifier is trained on Data1 and tested on Data2. AVG and STD denote the average and standard deviation. Mf is the median size of the optimal feature set. Column "Feature Extraction" indicates if features were obtained using H&E as well as IR data, or with H&E data alone. Column "Data set" indicates if the performance metrics are from training data (Data1) or from test data (Data2). The parameter γ of a radial basis kernel for SVM is set to 1.

Kwak et al. BMC Cancer 2011 11:62   doi:10.1186/1471-2407-11-62

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