# A Deep Learning based Pipeline for Efficient Oral Cancer Screening on Whole Slide Images

arXiv: https://arxiv.org/abs/1910.10549 · HTML: https://mlsanity.com/paper/1910.10549/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2019.

## Built on by (most cited first, 5 of 5)

- [What is the State of the Art of Computer Vision-Assisted Cytology? A Systematic Literature Review](https://mlsanity.com/paper/2105.11277/) (2021)
- [Let it shine: Autofluorescence of Papanicolaou-stain improves AI-based cytological oral cancer detection](https://mlsanity.com/paper/2407.01869/) (2024)
- [Oral cancer detection and interpretation: Deep multiple instance learning versus conventional deep single instance learning](https://mlsanity.com/paper/2202.01783/) (2022)
- [A Comparison of Deep Learning Methods for Cell Detection in Digital Cytology](https://mlsanity.com/paper/2504.06957/) (2025)
- [Needle in a Haystack: One-Class Representation Learning for Detecting Rare Malignant Cells in Computational Cytology](https://mlsanity.com/paper/2604.07722/) (2026)

## Abstract (arXiv)

Oral cancer incidence is rapidly increasing worldwide. The most important determinant factor in cancer survival is early diagnosis. To facilitate large scale screening, we propose a fully automated pipeline for oral cancer detection on whole slide cytology images. The pipeline consists of fully convolutional regression-based nucleus detection, followed by per-cell focus selection, and CNN based classification. Our novel focus selection step provides fast per-cell focus decisions at human-level accuracy. We demonstrate that the pipeline provides efficient cancer classification of whole slide cytology images, improving over previous results both in terms of accuracy and feasibility. The complete source code is available at https://github.com/MIDA-group/OralScreen.