  This article adresses the problem of automatic squamous cells classification
for cervical cancer screening using Deep Learning methods. We study different
architectures on a public dataset called Herlev dataset, which consists in
classifying cells, obtained by cervical pap smear, regarding the severity of
the abnormalities they represent. Furthermore, we use an attribution method to
understand which cytomorphological features are actually learned as
discriminative to classify severity of the abnormalities. Through this paper,
we show how we trained a performant classifier: 74.5\% accuracy on severity
classification and 94\% accuracy on normal/abnormal classification.
