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

2024-05-05

arXiv.org

AI Summary

To address low model reliability, difficulty in quantifying prediction uncertainty, and uninterpretable cross-view feature fusion in multi-view MRI-based liver fibrosis staging, this paper proposes MERIT, a multi-view evidential learning framework. MERIT is the first to introduce subjective logic theory into medical imaging multi-view analysis, enabling explicit uncertainty modeling and logically interpretable decision-making within a unified framework. It incorporates a distribution-aware prior estimation strategy and a feature-specific evidential combination rule to enhance robustness against class imbalance and disentangle individual view contributions. Evaluated on liver fibrosis MRI staging, MERIT reduces calibration error by 32% compared to baselines, supports immediate interpretation via view importance heatmaps, and enables post-hoc counterfactual analysis. The framework thus achieves both high reliability and intrinsic interpretability. The source code is publicly available.

Technology Category

• Machine Learning: Multi-instance/Multi-view Learning
• Reasoning under Uncertainty: Relational Probabilistic Models
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• Accurate liver fibrosis staging from MRI images.
• Uncertainty quantification in multi-view learning predictions.
• Improving interpretability of multi-view feature integration.

Innovation

• Multi-view evidential learning for uncertainty quantification
• Logic-based combination rule enhances model interpretability
• Distribution-aware base rate improves class shift performance