An causal XAI diagnostic model for breast cancer based on mammography reports

Breast cancer has become one of the most common malignant tumors in women worldwide, and it seriously threatens women's physical and mental health. In recent years, with the development of Artificial Intelligence(AI) and the accumulation of medical data, AI has begun to be deeply integrated with mammography, MRI, ultrasound, etc. to assist physicians in disease diagnosis. However, the existing breast cancer diagnosis model based on Computer Vision(CV) is greatly affected by the image quality; on the other hand, the breast cancer diagnosis model based on Natural Language Processing(NLP) cannot effectively extract the semantic information of the mammography report. The lack of model interpretability also makes the existing diagnostic models have low confidence. In this paper, we proposed Breast Cancer Causal XAI Diagnostic Model(BCCXDM). Specifically, we first structured the mammography report. Then find the causal graph based on the structured table. We combine the existing tabular learning method TabNet with causal graphs(Causal-TabNet) to enable reasoning in the graphs to preserve the correlation between features. More importantly, we use GNN and node transition probability to aggregate node information. We evaluate our model on the real-world mammography report, and compare it with other popular interpretable methods. The experimental results show that our interpretable results are closer to the diagnostic criteria of clinicians