MERIT: Multi-view Evidential learning for Reliable and Interpretable liver fibrosis sTaging
arxiv(2024)
摘要
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.
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