Abstract: The accuracy of computer-assisted diagnosis can be improved by using generative adversarial networks(GAN) to extend the data set of cervical cancer patholigical images. First， the cytoplasmic part of the histopathological images was generated by GAN; then， k-means clustering was used twice to select images generated by GAN; finally， Inception-V3 model was used to train a classifier. The results showed that the accuracy is improved by an average of 2.5% under the same test data set. Especially， it has significant effect for poorly differentiated cervical cancer pathological images. The non-directionality， complexity of content and poor regularity of foreground target for histopathological images are solved by GAN， which proves the effectiveness and the potential of this method.

Key words: auxiliary diagnosis for cervical cancer, histopathological image classification, generative adversarial networks(GAN), feature extracting, k-means clustering