H-MEAN: Hierarchical Multi-view Evidence Aggregation Network for Trustworthy Medical Image Classification

作者：Yina Li, Yufei Chen, Wei Liu, Jingen Qu, Chao Ma, Xiaodong Yue · 年份：2025 · DOI：10.1109/bibm66473.2025.11356753 · 被引用次数：3 · 研究领域：Adversarial Robustness in Machine Learning、Domain Adaptation and Few-Shot Learning、Explainable Artificial Intelligence (XAI)

Clinical disease classification naturally follows a hierarchical reasoning process, where clinicians first distinguish coarse diagnostic categories (e.g., benign vs. malignant) before identifying specific subtypes to enhance diagnostic accuracy. While multi-view imaging offers complementary perspectives, most existing machine learning approaches perform flat decision fusion, ignoring this intrinsic hierarchy and handling inter-view conflicts and uncertainty primarily at the decision-fusion level. To address this gap, we propose the Hierarchical Multi-view Evidence Aggregation Network (H-MEAN), an end-to-end architecture that explicitly mimics clinical decision-making process. H-MEAN consists of three key modules: (i) Multi-View Fusion, which jointly encodes heterogeneous imaging views to capture complementary features; (ii) Hierarchical Tree Alignment, which structurally propagates diagnostic cues across granularity levels to enforce clinical consistency; and (iii) Hierarchical Evidential Aggregation, which leverages evidential deep learning to quantify uncertainty throughout the diagnostic hierarchy. Extensive experiments on two multi-view medical datasets demonstrate that H-MEAN consistently outperforms competitive methods, achieving higher classification accuracy, particularly in challenging settings with missing or noisy views.