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.

翻译：细胞学是早期检测口腔鳞状细胞癌（OSCC）的重要工具。然而，对细胞学全玻片图像（WSIs）的人工检查速度慢、主观性强，且高度依赖病理学专家。为解决此问题，我们首次提出了一种用于口腔细胞学全玻片图像患者级别诊断的弱监督深度学习框架，利用了新发布的口腔细胞学数据集[1]，该数据集提供了来自印度十个医疗中心的带标注细胞学WSIs。每个患者病例被表示为一组细胞学图像块，并由内部专家病理学家分配诊断标签（健康、良性、口腔潜在恶性病变（OPMD）、OSCC）。这些患者级别的弱标签构成了数据集的新扩展。我们评估了一个基线多示例学习（MIL）模型和提出的区域亲和性注意力MIL（RAA-MIL），后者建模了每个玻片内区域间的空间关系。RAA-MIL在未见测试集上实现了平均准确率72.7%、加权F1分数0.69，优于基线模型。本研究建立了首个口腔细胞学患者级别弱监督基准，并朝着可靠的AI辅助数字病理学迈进一步。