用视觉Transformer模型提升乳腺癌影像检测准确率，最高达99.32%。
Fine-tuned Transformer Models for Breast Cancer Detection and Classification
- 采用Swin Tiny、ViT等视觉Transformer模型分析乳腺钼靶图像。
- ViT模型达到99.32%准确率，优于传统CNN。
- 适合医学影像分析与AI辅助诊断研究者参考。
乳腺癌仍是全球癌症死亡的第二大原因，早期检测至关重要。传统诊断方法如钼靶、超声和热成像在捕捉细微模式和降低假阳性方面存在局限。人工智能与深度学习技术正推动医学影像分析的变革。然而，典型架构如卷积神经网络（CNN）在建模长程依赖关系上表现不足。本研究探索了视觉Transformer模型（Swin Tiny、DeiT、BEiT、ViT及YOLOv8）在乳腺癌检测中的应用，基于一组钼靶图像数据集。其中，ViT模型达到99.32%的最高准确率，展现出对全局模式和细微特征的优越识别能力。通过图像缩放裁剪、翻转和归一化等数据增强策略进一步提升了性能。尽管结果令人鼓舞，数据集多样性与模型优化问题仍存，为未来研究提供新方向。该研究揭示了基于Transformer的AI模型在改变乳腺癌检测流程、改善患者健康方面的巨大潜力。
原文摘要 · Abstract (English)
Breast cancer is still the second top cause of cancer deaths worldwide and this emphasizes the importance of necessary steps for early detection. Traditional diagnostic methods, such as mammography, ultrasound, and thermography, which have limitations when it comes to catching subtle patterns and reducing false positives. New technologies like artificial intelligence (AI) and deep learning have brought about the revolution in medical imaging analysis. Nevertheless, typical architectures such as Convolutional Neural Networks (CNNs) often have problems with modeling long-range dependencies. It explores the application of visual transformer models (here: Swin Tiny, DeiT, BEiT, ViT, and YOLOv8) for breast cancer detection through a collection of mammographic image sets. The ViT model reached the highest accuracy of 99.32% which showed its superiority in detecting global patterns as well as subtle image features. Data augmenting approaches, such as resizing croppings, flippings, and normalization, were further applied to the model for achieving higher performance. Although there were interesting results, the issues of dataset diversity and model optimization which present new avenues of research are also still present. Through this study, the crystal potential of transformer-based AI models in changing the detecting process of breast cancer and, thus, to patients health, is suggested.
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