基于因果图注意力机制的乳腺癌图像分类

Breast Cancer Image Classification Based on Causal Graph Attention Mechanism

摘要: 乳腺癌是全球女性最常见的恶性肿瘤，对其发病机制和特性的深入研究对预防、早期筛查和治疗至关重要。本研究旨在通过提出一种新的因果发现注意力图神经网络(CA-GAT)模型，来提高乳腺癌图像分类的准确性和效率。首先，分析了现有图神经网络(GNN)在乳腺癌图像分类中的问题，特别是快捷特征与因果特征的混杂关系。为了解决这一问题，我们提出了CA-GAT模型，该模型通过三个主要部分：软掩模估计、混杂解开和因果干预，来强化因果特征与预测之间的因果关系。在BreaKHis数据集上进行实验，评估了CA-GAT模型的性能。实验结果表明，CA-GAT模型在乳腺癌图像分类任务中取得了93.8%的分类精度，显著优于其他传统GNN模型和深度学习模型。此外，通过Micro F1分数和Macro F1分数的比较，进一步证明了CA-GAT模型在分类准确性和类别间平衡性能上的优势。本研究提出的CA-GAT模型有效地提高了乳腺癌图像分类的准确性，为乳腺癌的诊断和治疗提供了一种新的工具。未来的工作将集中在进一步优化模型结构和探索更多实际应用场景。

Abstract: Breast cancer is the most common malignant tumor among women worldwide, and in-depth research on its pathogenesis and characteristics is crucial for prevention, early screening, and treatment. This study aims to improve the accuracy and efficiency of breast cancer image classification by proposing a novel Causal Discovery Attention Graph Neural Network (CA-GAT) model. The research begins with an analysis of the issues faced by existing Graph Neural Networks (GNNs) in breast cancer image classification, particularly the confounding relationship between shortcut features and causal features. To address this issue, the CA-GAT model is introduced, which reinforces the causal relationship between causal features and predictions through three main components: soft mask estimation, disentanglement of confounders, and causal intervention. Experiments were conducted on the BreaKHis dataset to evaluate the performance of the CA-GAT model. The results demonstrate that the CA-GAT model achieved a classification accuracy of 93.8% in breast cancer image classification tasks, significantly outperforming other traditional GNN models and deep learning models. Furthermore, comparisons of Micro F1 scores and Macro F1 scores further confirm the CA-GAT model’s advantages in classification accuracy and balanced performance across categories. The CA-GAT model proposed in this study effectively enhances the accuracy of breast cancer image classification, providing a new tool for the diagnosis and treatment of breast cancer. Future work will focus on further optimizing the model structure and exploring more practical application scenarios.