bibtext: @InProceedings{ Don_UncertaintyAware_MICCAI2024,
                   author = { Dong, Zhicheng and Yue, Xiaodong and Chen, Yufei and Zhou, Xujing and Liang, Jiye },
                   title = { { Uncertainty-Aware Multi-View Learning for Prostate Cancer Grading with DWI } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15010 },
                   month = {October},
                   pages = { pending },
                }
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.
title: Uncertainty-Aware Multi-View Learning for Prostate Cancer Grading with DWI
authors: Dong, Zhicheng Yue, Xiaodong Chen, Yufei Zhou, Xujing Liang, Jiye