用视觉Transformer提升宫颈癌筛查准确率与可解释性
Systematic Evaluation of Vision Transformers for Automated Cervical Cancer Classification: Optimization, Statistical Validation, and Clinical Interpretability
- 优化轻量ViT-Tiny模型，结合数据增强与类别权重
- 交叉验证准确率达94.9%-95.2%，优于传统CNN
- Grad-CAM显示注意力聚焦于细胞核等病理特征，适合临床应用
人工宫颈涂片分析受限于观察者差异、时间压力及专家短缺。尽管卷积神经网络（CNN）已用于自动细胞分类，但在建模长距离空间依赖性和临床可解释性方面仍不足。本研究系统优化了视觉变压器（ViT）架构以提升自动化宫颈癌筛查效果，显著增强了可解释性。采用Herlev数据集（917张图像：242正常，675异常）对轻量级ViT-Tiny进行综合评估，优化了数据增强策略、类别权重和超参数。最优配置下交叉验证准确率达到94.9%-95.2%，其中随机水平翻转和类别权重（0.7×1.3）最为有效。梯度加权类激活映射（Grad-CAM）分析表明，模型注意力与临床相关形态学特征一致，包括核区、细胞边界和染色质纹理，符合细胞病理学标准。结果表明，视觉变压器可提供兼具高精度与透明性的决策支持，满足医疗AI部署中性能与可解释性的双重需求。
原文摘要 · Abstract (English)
Manual Pap smear analysis for cervical cancer screening is limited by inter-observer variability, time constraints, and restricted expert availability. Although convolutional neural networks (CNNs) have automated cervical cell classification, they remain limited in modeling long-range spatial dependencies and often lack clinical interpretability. In this study, Vision Transformer (ViT) architectures were systematically optimized to enhance automated cervical cancer screening, which resulted in improved interpretability. The Herlev dataset (917 images: 242 normal, 675 abnormal) was utilized to optimize ViT-Tiny, a lightweight Vision Transformer architecture designed for reduced computational complexity, through a comprehensive evaluation of augmentation strategies, class weighting, and hyperparameters. The optimal configuration achieved 94.9%-95.2% cross-validation accuracy, in which random horizontal flipping and class weighting (0.7 x 1.3) were identified as most effective. Gradient-weighted Class Activation Mapping (Grad-CAM) analysis confirmed that model attention corresponded to clinically relevant morphological features, which include nuclear regions, cell boundaries, and chromatin texture, which align with cytopathological criteria. These findings indicate that Vision Transformers can deliver accurate and interpretable decision support for cervical cancer screening, which fulfills both clinical performance and transparency requirements essential for medical AI deployment.
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