Integrating Diagnosis Rules into Deep Neural Networks for Bladder Cancer Staging

Abstract

Bladder cancer is a malignant disease with substantial morbidity and mortality. Bladder cancer staging is crucial to determine the effective treatments of bladder tumors in clinic. As to the superiority of feature learning, Deep Convolutional Neural Networks (DCNN) are widely used to predict the cancer stage based on medical images. However, most existing DCNN-based cancer staging methods are data-driven and neglect the domain knowledge and experiences of clinicians. Besides, the deep neural networks are short of model interpretability and may lead to risky diagnosis. To tackle the problems, we construct the diagnosis rules of bladder cancer staging based on the clinical experiences of tumor penetration into bladder wall. The diagnosis rules are extracted from Magnetic Resonance (MR) images and further integrated into DCNN for joint identification of tumor stage. The experiments validate that the integrated rules improve the model interpretability and guide DCNN to focus on the regions of tumor penetration and thereby produce precise prediction of cancer staging.