用惩罚损失提升肺部X光多标签分类的临床可解释性
Clinically-Inspired Hierarchical Multi-Label Classification of Chest X-rays with a Penalty-Based Loss Function
- 设计分层交叉熵损失，强制诊断间临床依赖关系
- 在CheXpert数据集上达0.903平均AUROC
- 适合医学影像分析与可解释模型研究者
本文提出一种新型多标签胸部X光（CXR）图像分类方法，在保持单一模型、单次训练流程的同时，提升临床可解释性。基于CheXpert数据集和VisualCheXbert生成的标签，引入分层标签分组以捕捉诊断间的临床关联。为此，设计了自定义的分层二值交叉熵（HBCE）损失函数，通过固定或数据驱动的惩罚类型实现标签依赖约束。模型在测试集上达到0.903的平均受试者工作特征曲线下面积（AUROC）。此外，提供可视化解释与不确定性估计以增强模型可解释性。所有代码、模型配置及实验细节均已公开。
原文摘要 · Abstract (English)
In this work, we present a novel approach to multi-label chest X-ray (CXR) image classification that enhances clinical interpretability while maintaining a streamlined, single-model, single-run training pipeline. Leveraging the CheXpert dataset and VisualCheXbert-derived labels, we incorporate hierarchical label groupings to capture clinically meaningful relationships between diagnoses. To achieve this, we designed a custom hierarchical binary cross-entropy (HBCE) loss function that enforces label dependencies using either fixed or data-driven penalty types. Our model achieved a mean area under the receiver operating characteristic curve (AUROC) of 0.903 on the test set. Additionally, we provide visual explanations and uncertainty estimations to further enhance model interpretability. All code, model configurations, and experiment details are made available.
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