Accurate staging of liver fibrosis from magnetic resonance imaging (MRI) is
crucial in clinical practice. While conventional methods often focus on a
specific sub-region, multi-view learning captures more information by analyzing
multiple patches simultaneously. However, previous multi-view approaches could
not typically calculate uncertainty by nature, and they generally integrate
features from different views in a black-box fashion, hence compromising
reliability as well as interpretability of the resulting models. In this work,
we propose a new multi-view method based on evidential learning, referred to as
MERIT, which tackles the two challenges in a unified framework. MERIT enables
uncertainty quantification of the predictions to enhance reliability, and
employs a logic-based combination rule to improve interpretability.
Specifically, MERIT models the prediction from each sub-view as an opinion with
quantified uncertainty under the guidance of the subjective logic theory.
Furthermore, a distribution-aware base rate is introduced to enhance
performance, particularly in scenarios involving class distribution shifts.
Finally, MERIT adopts a feature-specific combination rule to explicitly fuse
multi-view predictions, thereby enhancing interpretability. Results have
showcased the effectiveness of the proposed MERIT, highlighting the reliability
and offering both ad-hoc and post-hoc interpretability. They also illustrate
that MERIT can elucidate the significance of each view in the decision-making
process for liver fibrosis staging.

该研究提出了一种基于证据学习的多视角方法（MERIT），具备对预测结果不确定性的量化和可解释性，对于肝纤维化分期决策过程中每个视角的重要性提供了阐明。

MERIT: 可靠且可解释的肝纤维化分级的多视图证据学习

MERIT: Multi-view Evidential learning for Reliable and Interpretable  liver fibrosis sTaging

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.

本研究提出一种基于多视角学习的深度学习方法，利用肝脏的多个子区域来提取更丰富的特征，使用主观逻辑来评估不确定性并应用 Dempster-Shafer 证据理论的明确组合规则来提高预测的准确性，通过在增强 MRI 数据上的评估结果发现，本方法优于现有的多视角学习方法。