  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.
