南方医科大学学报2026，Vol.46Issue(6)：1434-1443,10.DOI:10.12122/j.issn.1673-4254.2026.06.23
基于多约束潜在表征学习的双向特征映射分类模型:肺炎鉴别诊断
A bidirectional feature mapping classification model based on multi-constrained latent representation learning for differential diagnosis of pneumonia
摘要
Abstract
Objective To develop a bidirectional feature-mapping classification model for differential diagnosis of pneumonia.Methods We collected chest X-ray(CXR)images from 1457 patients with pneumonia and 1456 healthy individuals.Radiomic features extracted from the segmentation masks using PyRadiomics were mapped into a latent shared space to construct the classification model using the bidirectional feature mapping classification model based on multi-constrained latent representation learning.The performance of the constructed model for differential diagnosis of pneumonia was evaluated using 5-fold cross-validation and compared with other feature-based classification models.Decision curve analysis was used for evaluating clinical utility of the model,and ablation experiments were performed to assess the contribution of each constraint module.The importance of the radiomics features was interpreted using the SHAP method,and a two-dimensional visualization experiment of the low-dimensional latent features obtained through the proposed mapping method was conducted to verify the feasibility and effectiveness of the model.Results The 5-fold cross-validation results showed that the proposed classification model had a positive predictive value of 0.796,a negative predictive value of 0.830,a specificity of 0.784,a sensitivity of 0.830,an accuracy of 0.811,and an area under the ROC curve of 0.893 for differential diagnosis of pneumonia.Decision curve analysis demonstrated a high net clinical benefit of the model within acceptable threshold probabilities.Ablation studies confirmed the essential role of the multi-constraint module,and the SHAP analysis revealed that the model focused primarily on clinically meaningful and medically interpretable features.The feature mapping method exhibited excellent performance in visual experiments to confirm the effectiveness of the proposed model.Conclusion The proposed bidirectional feature mapping classification model demonstrates strong discriminative capability and high potential for differential diagnosis of pneumonia and shows obvious advantages over other classification models in pneumonia classification tasks.关键词
影像组学/特征映射/表征学习/判别分析/肺炎Key words
radiomics/feature mapping/representation learning/discriminant analysis/pneumonia引用本文复制引用
曾敏,卓俐,谭顺谦,甄鑫..基于多约束潜在表征学习的双向特征映射分类模型:肺炎鉴别诊断[J].南方医科大学学报,2026,46(6):1434-1443,10.基金项目
国家自然科学基金(82572381) （82572381）
广东省自然科学基金(2024A1515012100) Supported by National Natural Science Foundation of China(82572381). （2024A1515012100）
