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

• URL: http://arxiv.org/abs/2306.12054v1
• Date: Wed, 21 Jun 2023 06:53:51 GMT
• Title: A Reliable and Interpretable Framework of Multi-view Learning for Liver Fibrosis Staging
• Authors: Zheyao Gao, Yuanye Liu, Fuping Wu, NanNan Shi, Yuxin Shi, Xiahai Zhuang
• Abstract summary: 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. We formulate this task as a multi-view learning problem and employ multiple sub-regions of the liver.
• Score: 13.491056805108183
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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