首个弱监督框架，用AI自动诊断口腔癌细胞切片。
RAA-MIL: A Novel Framework for Classification of Oral Cytology
- 用区域关联注意力建模切片内空间关系，提升分类精度。
- 在未见测试集上达72.7%准确率，加权F1为0.69。
- 适合医学影像分析与数字病理领域研究者参考。
细胞学是早期发现口腔鳞状细胞癌（OSCC）的重要工具。然而，人工审阅细胞学全切片图像（WSIs）耗时、主观且高度依赖专家病理科医生。为此，我们提出首个针对患者级诊断的弱监督深度学习框架，基于新发布的印度十家医疗中心的口腔细胞学数据集[1]。每个患者病例被表示为一组细胞切片块，并由院内病理科专家标注诊断标签（健康、良性、口腔潜在恶性病变（OPMD）、OSCC）。这些患者级弱标签构成数据集的新扩展。我们评估了基线多实例学习（MIL）模型和提出的区域关联注意力多实例学习（RAA-MIL）模型，后者通过建模每张切片内区域间的空间关系实现改进。RAA-MIL在未见测试集上平均准确率达72.7%，加权F1得分为0.69，优于基线。本研究建立了口腔细胞学首个患者级弱监督基准，推动人工智能辅助数字病理的发展。
原文摘要 · Abstract (English)
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.
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