A Novel Computer-Aided Diagnosis Scheme on Small Annotated Set: G2C-CAD.PURPOSE
 To solve this problem, in this study, we adopt a generative adversarial network (GAN) approach and design a semisupervised learning algorithm, named G2C-CAD.


 Finally, we tested the fuzzy Co-forest and compared its performance with that of a C4.5 random decision forest and the G2C-CAD system without the fuzzy scheme, using ROC and confusion matrix for evaluation.


 For these five classes, the G2C-CAD system obtained AUCs of 0.946, 0.912, 0.908, 0.887, and 0.939, respectively.  The average accuracy of G2C-CAD exceeded that of the C4.5 random decision tree by 14%.  G2C-CAD also obtained promising test results on the LISS signs dataset; its AUCs for GGO, lobulation, spiculation, pleural indentation, and negative image samples were 0.972, 0.964, 0.941, 0.967, and 0.953, respectively.


 The experimental results show that G2C-CAD is an appropriate method for addressing the problem of insufficient labeled samples in the medical image analysis field. 
G2C-CAD
To solve this problem, in this study, we adopt a generative adversarial network (GAN) approach and design a semisupervised learning algorithm, named G2C-CAD.


 We split the data 90% for training and 10% for testing.  We designed a DCGAN generative adversarial framework and trained it on the small sample set.  Then, coupled with the simulated unlabeled samples generated by the trained DCGAN, we conducted iterative semisupervised learning, which continually improved the classification performance of the fuzzy Co-forest until the termination condition was reached. 
GAN
We designed a DCGAN generative adversarial framework and trained it on the small sample set.  Then, coupled with the simulated unlabeled samples generated by the trained DCGAN, we conducted iterative semisupervised learning, which continually improved the classification performance of the fuzzy Co-forest until the termination condition was reached. 
DCGAN
We also trained our designed CNN-based fuzzy Co-forest on the labeled small sample set and obtained a preliminary classifier. 
CNN-based fuzzy Co-forest
Finally, we tested the fuzzy Co-forest and compared its performance with that of a C4.5 random decision forest and the G2C-CAD system without the fuzzy scheme, using ROC and confusion matrix for evaluation.



C4.5 random decision forest