  Screening is critical for prevention and early detection of cervical cancer
but it is time-consuming and laborious. Supervised deep convolutional neural
networks have been developed to automate pap smear screening and the results
are promising. However, the interest in using only normal samples to train deep
neural networks has increased owing to class imbalance problems and
high-labeling costs that are both prevalent in healthcare. In this study, we
introduce a method to learn explainable deep cervical cell representations for
pap smear cytology images based on one class classification using variational
autoencoders. Findings demonstrate that a score can be calculated for cell
abnormality without training models with abnormal samples and localize
abnormality to interpret our results with a novel metric based on absolute
difference in cross entropy in agglomerative clustering. The best model that
discriminates squamous cell carcinoma (SCC) from normals gives 0.908 +- 0.003
area under operating characteristic curve (AUC) and one that discriminates
high-grade epithelial lesion (HSIL) 0.920 +- 0.002 AUC. Compared to other
clustering methods, our method enhances the V-measure and yields higher
homogeneity scores, which more effectively isolate different abnormality
regions, aiding in the interpretation of our results. Evaluation using in-house
and additional open dataset show that our model can discriminate abnormality
without the need of additional training of deep models.
