A Nasal Cytology Dataset for Object Detection and Deep Learning

TL;DR

This paper introduces the first nasal cytology dataset for training object detection models to automate cell counting, aiming to improve diagnosis of rhinitis and allergies through AI techniques.

Contribution

It provides a novel dataset of nasal cytology images with annotations and evaluates deep learning models like DETR and YOLO for cell detection and classification.

Findings

DETR and YOLO models achieved good detection accuracy.

The dataset captures the real distribution of nasal mucosa cells.

Addressed class imbalance issues in cell detection.

Abstract

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.