仅用胸片识别严重左心室肥厚，不依赖年龄性别等信息
Encoding of Demographic and Anatomical Information in Chest X-Ray-based Severe Left Ventricular Hypertrophy Classifiers
- 直接从胸片分类，无需解剖测量或人口统计信息
- AUROC和AUPRC均表现优异，模型性能强
- 用互信息估计量化特征表达能力，解释透明
尽管超声心动图和磁共振成像是评估心脏结构的临床标准，但其应用受限于成本与可及性。我们提出一种直接分类框架，仅通过胸片预测严重左心室肥厚，不依赖解剖测量或人口统计输入。该方法在多个指标上达到高表现，采用互信息神经估计（Mutual Information Neural Estimation）量化特征表达能力，揭示了临床有意义的属性编码，支持模型的透明化解释。
原文摘要 · Abstract (English)
While echocardiography and MRI are clinical standards for evaluating cardiac structure, their use is limited by cost and accessibility.We introduce a direct classification framework that predicts severe left ventricular hypertrophy from chest X-rays, without relying on anatomical measurements or demographic inputs. Our approach achieves high AUROC and AUPRC, and employs Mutual Information Neural Estimation to quantify feature expressivity. This reveals clinically meaningful attribute encoding and supports transparent model interpretation.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。