核心概念
This work presents the first publicly available dataset of nasal cytology images, the Nasal Cytology Dataset (NCD), to enable the development of deep learning models for automated detection and classification of nasal mucosa cells.
摘要
This paper introduces the Nasal Cytology Dataset (NCD), a novel dataset of over 10,000 annotated instances of nasal mucosa cells across 500 microscopic images. The dataset was constructed by experts in otolaryngology and computer science to address the challenge of automating the analysis of nasal cytology, a clinical technique for diagnosing rhinitis and allergies.
The dataset covers 10 different cell types found in the nasal mucosa, including epithelial cells, ciliated cells, metaplastic cells, muciparous cells, neutrophils, eosinophils, lymphocytes, mast cells, erythrocytes, and artifacts. The images were acquired using an optical microscope and annotated by experts, with bounding boxes and class labels for each cell.
The authors evaluated the performance of two deep learning models, DETR and YOLOv8, on two tasks: cell recognition (classifying cells into their respective cytotypes) and cell detection (identifying the presence of cells in the images). The results showed that while the models performed well on detecting cells, the cell recognition task was more challenging due to the class imbalance in the dataset, with some rare cell types having very few examples.
The authors discuss the importance of addressing this class imbalance and the potential for splitting the task into two stages: cell detection and cell classification. They also highlight the potential for the NCD dataset to serve as a benchmark for developing and evaluating AI-based approaches to support rhinology experts in their clinical practice.
統計資料
The dataset contains 10,060 instances of cells across 10 different cytotypes.
The most common cell type is epithelial cells, accounting for 50.3% of the instances.
The rarest cell types are mast cells (0.2%) and emazie (0.5%).
引述
"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."
"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 [1] and YOLO [2] models shown good performance in detecting cells and classifying them correctly, revealing great potential to accelerate the work of rhinology experts."