用因果推理提升医学影像质量评估的可靠性。
Medical Image Quality Assessment based on Probability of Necessity and Sufficiency
- 基于必要充分概率学习影像关键特征，避免虚假关联
- 在AS-OCT数据集上显著提升对分布外样本的鲁棒性
- 适合关注模型可解释性与泛化能力的研究者
医学图像质量评估（MIQA）对可靠医疗影像分析至关重要。尽管深度学习在此领域展现潜力，但现有模型易受数据中虚假相关性干扰，且在分布外（OOD）场景下表现不佳。为此，本文提出一种基于因果推断概念——必要充分概率（PNS）的MIQA框架。PNS衡量一组特征对特定结果的必要性（始终存在）与充分性（能保证结果）程度。该方法通过学习具有高PNS值的隐含特征进行质量预测，促使模型捕捉更本质的预测信息，增强对OOD场景的鲁棒性。在前段光学相干断层扫描（AS-OCT）数据集上的实验验证了该框架的有效性。
原文摘要 · Abstract (English)
Medical image quality assessment (MIQA) is essential for reliable medical image analysis. While deep learning has shown promise in this field, current models could be misled by spurious correlations learned from data and struggle with out-of-distribution (OOD) scenarios. To that end, we propose an MIQA framework based on a concept from causal inference: Probability of Necessity and Sufficiency (PNS). PNS measures how likely a set of features is to be both necessary (always present for an outcome) and sufficient (capable of guaranteeing an outcome) for a particular result. Our approach leverages this concept by learning hidden features from medical images with high PNS values for quality prediction. This encourages models to capture more essential predictive information, enhancing their robustness to OOD scenarios. We evaluate our framework on an Anterior Segment Optical Coherence Tomography (AS-OCT) dataset for the MIQA task and experimental results demonstrate the effectiveness of our framework.
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