# A Reliable and Interpretable Framework of Multi-view Learning for Liver Fibrosis Staging

arXiv: https://arxiv.org/abs/2306.12054 · HTML: https://mlsanity.com/paper/2306.12054/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 26 references that are in the map.
- First posted on arXiv 21 Jun 2023.

## Builds on (most cited first, 17 of 17)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Improved Regularization of Convolutional Neural Networks with Cutout](https://mlsanity.com/paper/1708.04552/) (2017)
- [Contrastive Multiview Coding](https://mlsanity.com/paper/1906.05849/) (2019)
- [Attention-based Deep Multiple Instance Learning](https://mlsanity.com/paper/1802.04712/) (2018)
- [Two-Stream Convolutional Networks for Action Recognition in Videos](https://mlsanity.com/paper/1406.2199/) (2014)
- [Evidential Deep Learning to Quantify Classification Uncertainty](https://mlsanity.com/paper/1806.01768/) (2018)
- [Convolutional Two-Stream Network Fusion for Video Action Recognition](https://mlsanity.com/paper/1604.06573/) (2016)
- [A Survey on Neural Network Interpretability](https://mlsanity.com/paper/2012.14261/) (2020)
- [Trusted Multi-View Classification with Dynamic Evidential Fusion](https://mlsanity.com/paper/2204.11423/) (2022)
- [On Deep Multi-View Representation Learning: Objectives and Optimization](https://mlsanity.com/paper/1602.01024/) (2016)
- [Vision Transformer for Small-Size Datasets](https://mlsanity.com/paper/2112.13492/) (2021)
- [Masksembles for Uncertainty Estimation](https://mlsanity.com/paper/2012.08334/) (2020)
- [Understanding Softmax Confidence and Uncertainty](https://mlsanity.com/paper/2106.04972/) (2021)
- [Towards Learning Convolutions from Scratch](https://mlsanity.com/paper/2007.13657/) (2020)
- [UNO: Uncertainty-aware Noisy-Or Multimodal Fusion for Unanticipated Input Degradation](https://mlsanity.com/paper/1911.05611/) (2019)

## Built on by (most cited first, 13 of 13)

- [A Comprehensive Survey on Evidential Deep Learning and Its Applications](https://mlsanity.com/paper/2409.04720/) (2024)
- [MERIT: Multi-view evidential learning for reliable and interpretable liver fibrosis staging](https://mlsanity.com/paper/2405.02918/) (2024)
- [EviPrompt: A Training-Free Evidential Prompt Generation Method for Segment Anything Model in Medical Images](https://mlsanity.com/paper/2311.06400/) (2023)
- [Multi-modal MRI Translation via Evidential Regression and Distribution Calibration](https://mlsanity.com/paper/2407.07372/) (2024)
- [Semi-supervised Liver Segmentation and Patch-based Fibrosis Staging with Registration-aided Multi-parametric MRI](https://mlsanity.com/paper/2602.09686/) (2026)
- [How Far Has AI Come in Liver Fibrosis Staging? A Large-Scale Real-World Dataset and Benchmark](https://mlsanity.com/paper/2605.25595/) (2026)
- [Improved mmFormer for Liver Fibrosis Staging via Missing-Modality Compensation](https://mlsanity.com/paper/2509.16436/) (2025)
- [Multi-modal Liver Segmentation and Fibrosis Staging Using Real-world MRI Images](https://mlsanity.com/paper/2509.26061/) (2025)
- [The 1st Solution for CARE Liver Task Challenge 2025: Contrast-Aware Semi-Supervised Segmentation with Domain Generalization and Test-Time Adaptation](https://mlsanity.com/paper/2510.04243/) (2025)
- [Label-Efficient Cross-Modality Generalization for Liver Segmentation in Multi-Phase MRI](https://mlsanity.com/paper/2510.04705/) (2025)
- [SSL-MedSAM2: A Semi-supervised Medical Image Segmentation Framework Powered by Few-shot Learning of SAM2](https://mlsanity.com/paper/2512.11548/) (2025)
- [Non-Contrast CT Esophageal Varices Grading through Clinical Prior-Enhanced Multi-Organ Analysis](https://mlsanity.com/paper/2512.19415/) (2025)
- [DINO-Med: A Unified Patch-Based Adaptation Framework for Multi-Modal Medical Image Analysis Applied to Liver Fibrosis Staging](https://mlsanity.com/paper/2609.11380/) (2026)

## Abstract (arXiv)

Staging of liver fibrosis is important in the diagnosis and treatment planning of patients suffering from liver diseases. Current deep learning-based methods using abdominal magnetic resonance imaging (MRI) usually take a sub-region of the liver as an input, which nevertheless could miss critical information. To explore richer representations, we formulate this task as a multi-view learning problem and employ multiple sub-regions of the liver. Previously, features or predictions are usually combined in an implicit manner, and uncertainty-aware methods have been proposed. However, these methods could be challenged to capture cross-view representations, which can be important in the accurate prediction of staging. Therefore, we propose a reliable multi-view learning method with interpretable combination rules, which can model global representations to improve the accuracy of predictions. Specifically, the proposed method estimates uncertainties based on subjective logic to improve reliability, and an explicit combination rule is applied based on Dempster-Shafer's evidence theory with good power of interpretability. Moreover, a data-efficient transformer is introduced to capture representations in the global view. Results evaluated on enhanced MRI data show that our method delivers superior performance over existing multi-view learning methods.