  Cervical intraepithelial neoplasia (CIN) grade of histopathology images is a
crucial indicator in cervical biopsy results. Accurate CIN grading of
epithelium regions helps pathologists with precancerous lesion diagnosis and
treatment planning. Although an automated CIN grading system has been desired,
supervised training of such a system would require a large amount of expert
annotations, which are expensive and time-consuming to collect. In this paper,
we investigate the CIN grade classification problem on segmented epithelium
patches. We propose to use conditional Generative Adversarial Networks (cGANs)
to expand the limited training dataset, by synthesizing realistic cervical
histopathology images. While the synthetic images are visually appealing, they
are not guaranteed to contain meaningful features for data augmentation. To
tackle this issue, we propose a synthetic-image filtering mechanism based on
the divergence in feature space between generated images and class centroids in
order to control the feature quality of selected synthetic images for data
augmentation. Our models are evaluated on a cervical histopathology image
dataset with a limited number of patch-level CIN grade annotations. Extensive
experimental results show a significant improvement of classification accuracy
from 66.3% to 71.7% using the same ResNet18 baseline classifier after
leveraging our cGAN generated images with feature-based filtering, which
demonstrates the effectiveness of our models.
