# Regression Constraint for an Explainable Cervical Cancer Classifier

arXiv: https://arxiv.org/abs/1908.02650 · HTML: https://mlsanity.com/paper/1908.02650/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Aug 2019.

## Built on by (most cited first, 1 of 1)

- [Systematic Evaluation of Vision Transformers for Automated Cervical Cancer Classification: Optimization, Statistical Validation, and Clinical Interpretability](https://mlsanity.com/paper/2605.17236/) (2026)

## Abstract (arXiv)

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.