Uncertainty-Aware Multi-view Learning for Prostate Cancer Grading with DWI

Zhicheng Dong, Xiaodong Yue, Yufei Chen, Xujing Zhou, Jiye Liang

Abstract: Grading of prostate cancer plays an important role in the planning of surgery and prognosis. Multi-parametric magnetic resonance imaging (mp-MRI) of the prostate can facilitate the detection, localization and grade of prostate cancer. In mp-MRI, Diffusion-Weighted Imaging (DWI) can distinguish a malignant neoplasm from benign prostate tissue due to a significant difference in the apparent diffusion sensitivity coefficient (b-value). DWI using high b-value is preferred for prostate cancer grading, providing high accuracy despite a decrease signal-to-noise ratio and increased image distortion. On the other hand, low b-value could avoid confounding pseudo-perfusion effects but in which the prostate normal parenchyma shows a very high signal intensity, making it difficult to distinguish it from prostate cancer foci. To fully capitalize on the advantages and information of DWIs with different b-values, we formulate the prostate cancer grading as a multi-view classification problem, treating DWIs with different b-values as distinct views. Multi-view classification aims to integrate views into a unified and comprehensive representation. However, existing multi-view methods cannot quantify the uncertainty of views and lack a interpretable and reliable fusion rule. To tackle this problem, we propose uncertainty-aware multi-view classification with uncertainty-aware belief integration. We measure the uncertainty of DWI based on Evidential Deep Learning and propose a novel strategy of uncertainty-aware belief integration to fuse multiple DWIs based on uncertainty measurements. Results demonstrate that our method outperforms current multi-view learning methods, showcasing its superior performance.