RAA-MIL: A Novel Framework for Classification of Oral Cytology

TL;DR

This paper introduces RAA-MIL, a weakly supervised deep learning framework that improves patient-level diagnosis of oral cytology WSIs, leveraging spatial relationships between regions to outperform baseline models.

Contribution

The study presents the first weakly supervised deep learning framework for oral cytology diagnosis, incorporating spatial relationships via RAA-MIL and establishing a new benchmark dataset.

Findings

RAA-MIL achieves 72.7% accuracy on unseen test data.

The model outperforms baseline MIL models.

Establishes a new benchmark for weakly supervised oral cytology diagnosis.

Abstract

Cytology is a valuable tool for early detection of oral squamous cell carcinoma (OSCC). However, manual examination of cytology whole slide images (WSIs) is slow, subjective, and depends heavily on expert pathologists. To address this, we introduce the first weakly supervised deep learning framework for patient-level diagnosis of oral cytology whole slide images, leveraging the newly released Oral Cytology Dataset [1], which provides annotated cytology WSIs from ten medical centres across India. Each patient case is represented as a bag of cytology patches and assigned a diagnosis label (Healthy, Benign, Oral Potentially Malignant Disorders (OPMD), OSCC) by an in-house expert pathologist. These patient-level weak labels form a new extension to the dataset. We evaluate a baseline multiple-instance learning (MIL) model and a proposed Region-Affinity Attention MIL (RAA-MIL) that models spatial relationships between regions within each slide. The RAA-MIL achieves an average accuracy of 72.7%, weighted F1-score of 0.69 on an unseen test set, outperforming the baseline. This study establishes the first patient-level weakly supervised benchmark for oral cytology and moves toward reliable AI-assisted digital pathology.