MERIT: Multi-view evidential learning for reliable and interpretable liver fibrosis staging

TL;DR

MERIT introduces a multi-view evidential learning framework for liver fibrosis staging from MRI, providing reliable uncertainty quantification and interpretability by modeling view-specific opinions and explicitly fusing multi-view predictions.

Contribution

It presents a novel evidential multi-view method that enhances reliability and interpretability in liver fibrosis staging, addressing uncertainty estimation and transparent decision-making.

Findings

MERIT improves prediction reliability with uncertainty quantification.

It enhances interpretability through logic-based multi-view fusion.

The method effectively elucidates view importance in staging decisions.

Abstract

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. Our code has be released via https://github.com/HenryLau7/MERIT.