Staging of liver fibrosis is important in the diagnosis and treatment planning of patients suffering from liver diseases. Current deep learning-based methods using abdominal magnetic resonance imaging (MRI) usually take a sub-region of the liver as an input, which nevertheless could miss critical information. To explore richer representations, we formulate this task as a multi-view learning problem and employ multiple sub-regions of the liver. Previously, features or predictions are usually combined in an implicit manner, and uncertainty-aware methods have been proposed. However, these methods could be challenged to capture cross-view representations, which can be important in the accurate prediction of staging. Therefore, we propose a reliable multi-view learning method with interpretable combination rules, which can model global representations to improve the accuracy of predictions. Specifically, the proposed method estimates uncertainties based on subjective logic to improve reliability, and an explicit combination rule is applied based on Dempster-Shafer's evidence theory with good power of interpretability. Moreover, a data-efficient transformer is introduced to capture representations in the global view. Results evaluated on enhanced MRI data show that our method delivers superior performance over existing multi-view learning methods.

翻译：肝纤维化分期在肝病患者的诊断和治疗规划中至关重要。当前基于深度学习的腹部磁共振成像（MRI）方法通常将肝脏的单一子区域作为输入，但这可能会遗漏关键信息。为探索更丰富的表示，我们将该任务建模为多视角学习问题，并采用多个肝脏子区域。以往的工作通常以隐式方式融合特征或预测，也有研究提出了不确定性感知方法。然而，这些方法难以有效捕获跨视角表示，而跨视角表示对于精确分期预测至关重要。因此，我们提出了一种具有可解释组合规则的可靠多视角学习方法，能够建模全局表示以提升预测准确性。具体而言，该方法基于主观逻辑估计不确定性以提高可靠性，并采用基于Dempster-Shafer证据理论的显式组合规则，具备良好的可解释性。此外，我们引入了一种数据高效的Transformer以捕获全局视角下的表示。在增强MRI数据上的评估结果表明，我们的方法优于现有的多视角学习方法。