用视觉变压器与图神经网络提升乳腺癌检测准确率
Enhancing Breast Cancer Detection with Vision Transformers and Graph Neural Networks
- 融合ViT与GNN，同时捕捉图像全局特征和结构关系
- 在CBIS-DDSM数据集上达到84.2%的检测准确率
- 生成可解释注意力热图，辅助医生临床决策
乳腺癌是全球女性主要死因之一，早期检测对提高生存率至关重要。本文提出一种创新框架，结合视觉变压器（ViT）与图神经网络（GNN），利用CBIS-DDSM数据集进行乳腺癌检测。该框架充分发挥ViT捕捉全局图像特征的能力和GNN建模结构关系的优势，在测试中实现84.2%的准确率，优于传统方法。此外，模型生成的可解释注意力热图能揭示决策过程，有助于放射科医生在临床中理解结果。
原文摘要 · Abstract (English)
Breast cancer is a leading cause of death among women globally, and early detection is critical for improving survival rates. This paper introduces an innovative framework that integrates Vision Transformers (ViT) and Graph Neural Networks (GNN) to enhance breast cancer detection using the CBIS-DDSM dataset. Our framework leverages ViT's ability to capture global image features and GNN's strength in modeling structural relationships, achieving an accuracy of 84.2%, outperforming traditional methods. Additionally, interpretable attention heatmaps provide insights into the model's decision-making process, aiding radiologists in clinical settings.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。