Robust Multi-modal Prostate Cancer Classification via Feature Disentanglement and Dual Attention

Abstract

Prostate cancer is the second leading cause of cancer death in men. At present, the methods for classifying early cancer on MRI images are mainly focused on single image modality and with low robustness. Therefore, this paper focuses on the method of classifying prostate cancer grade on multi-modality MRI images and maintaining robustness. In this paper, we propose a novel and effective multi-modal convolutional neural network for discriminating prostate cancer clinical severity grade, i.e. robust multimodal feature disentanglement attention net(RMDANet), and greatly improve the accuracy and robustness. T2-weighted(T2) and Diffusion-weighted imaging(DWI) are mainly used in this article. Experiments were conducted on the ProstateX dataset and augmented with hospital data, By comparing with other baseline methods, multi-modal dual input methods, SOTA methods, the AUC values obtained by the proposed model in this paper after the test set are higher than those of other classical models, the AUC value reached 0.835.