MERIT: Multi-view Evidential learning for Reliable and Interpretable liver fibrosis sTaging
<|reference_start|>MERIT: Multi-view Evidential learning for Reliable and Interpretable liver fibrosis sTaging: 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.<|reference_end|>
@article{liu2024merit:,
    title={MERIT: Multi-view Evidential learning for Reliable and Interpretable
  liver fibrosis sTaging},
    author={Yuanye Liu, Zheyao Gao, Nannan Shi, Fuping Wu, Yuxin Shi, Qingchao
  Chen, Xiahai Zhuang},
    journal={arXiv preprint arXiv:2405.02918},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.02918},
    primaryClass={cs.CV}
}