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

Journal: Medical image analysis
Published Date:

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 will be released via https://github.com/HenryLau7/MERIT.

Authors

  • Yuanye Liu
    School of Data Science, Fudan University, Shanghai, 200433, China.
  • Zheyao Gao
  • Nannan Shi
    Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, 201508, China.
  • Fuping Wu
    School of Data Science, Fudan University, Shanghai, China; Dept of Statistics, School of Management, Fudan University, Shanghai, China.
  • Yuxin Shi
    Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China. shiyx828288@163.com.
  • Qingchao Chen
    Institute of Biomedical Engineering (IBME), University of Oxford, Oxford, England, UK.
  • Xiahai Zhuang
    School of Data Science, Fudan University, Shanghai, China; Fudan-Xinzailing Joint Research Center for Big Data, Fudan University, Shanghai, China. Electronic address: zxh@fudan.edu.cn.