# A Nasal Cytology Dataset for Object Detection and Deep Learning

arXiv: https://arxiv.org/abs/2404.13745 · HTML: https://mlsanity.com/paper/2404.13745/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 39 references that are in the map.
- First posted on arXiv 21 Apr 2024.

## Builds on (most cited first, 5 of 5)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Deep Learning for Computational Cytology: A Survey](https://mlsanity.com/paper/2202.05126/) (2022)
- [Cytology Image Analysis Techniques Towards Automation: Systematically Revisited](https://mlsanity.com/paper/2003.07529/) (2020)
- [What is the State of the Art of Computer Vision-Assisted Cytology? A Systematic Literature Review](https://mlsanity.com/paper/2105.11277/) (2021)

## Abstract (arXiv)

Nasal Cytology is a new and efficient clinical technique to diagnose rhinitis and allergies that is not much widespread due to the time-consuming nature of cell counting; that is why AI-aided counting could be a turning point for the diffusion of this technique. In this article we present the first dataset of rhino-cytological field images: the NCD (Nasal Cytology Dataset), aimed to train and deploy Object Detection models to support physicians and biologists during clinical practice. The real distribution of the cytotypes, populating the nasal mucosa has been replicated, sampling images from slides of clinical patients, and manually annotating each cell found on them. The correspondent object detection task presents non'trivial issues associated with the strong class imbalancement, involving the rarest cell types. This work contributes to some of open challenges by presenting a novel machine learning-based approach to aid the automated detection and classification of nasal mucosa cells: the DETR and YOLO models shown good performance in detecting cells and classifying them correctly, revealing great potential to accelerate the work of rhinology experts.