  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.
