A Cytology Dataset for Early Detection of Oral Squamous Cell Carcinoma

TL;DR

This paper introduces the first large, multicenter oral cytology dataset with expert annotations, aiming to facilitate AI-driven early detection of oral squamous cell carcinoma, especially in resource-limited settings.

Contribution

It provides a comprehensive, labeled dataset of oral cytology slides from multiple centers, addressing the lack of publicly available data for AI model development in OSCC detection.

Findings

Dataset includes annotated slides stained with PAP and MGG protocols.

Collected from ten medical centers across India.

Designed to improve AI-based early diagnosis of OSCC.

Abstract

Oral squamous cell carcinoma OSCC is a major global health burden, particularly in several regions across Asia, Africa, and South America, where it accounts for a significant proportion of cancer cases. Early detection dramatically improves outcomes, with stage I cancers achieving up to 90 percent survival. However, traditional diagnosis based on histopathology has limited accessibility in low-resource settings because it is invasive, resource-intensive, and reliant on expert pathologists. On the other hand, oral cytology of brush biopsy offers a minimally invasive and lower cost alternative, provided that the remaining challenges, inter observer variability and unavailability of expert pathologists can be addressed using artificial intelligence. Development and validation of robust AI solutions requires access to large, labeled, and multi-source datasets to train high capacity models that generalize across domain shifts. We introduce the first large and multicenter oral cytology dataset, comprising annotated slides stained with Papanicolaou(PAP) and May-Grunwald-Giemsa(MGG) protocols, collected from ten tertiary medical centers in India. The dataset is labeled and annotated by expert pathologists for cellular anomaly classification and detection, is designed to advance AI driven diagnostic methods. By filling the gap in publicly available oral cytology datasets, this resource aims to enhance automated detection, reduce diagnostic errors, and improve early OSCC diagnosis in resource-constrained settings, ultimately contributing to reduced mortality and better patient outcomes worldwide.