融合单模态与视觉语言表征的胸部X光多标签分类
原标题：Integrating Unimodal and Vision-Language Representations in Latent Space for Multi-Label Chest X-Ray Classification
AI 摘要
该研究提出一个融合框架，将RAD-DINO的单模态视觉表征与BioViL-T的视觉-语言表征在潜在空间中分别精炼后归一化并跨三分支融合，用于MIMIC-CXR-JPG数据集上14类胸部X光多标签分类。实验显示RAD-DINO单独使用时优于BioViL-T，而早期融合进一步提升结果，表明两种嵌入来源包含互补信息；混合融合在潜在空间精炼后相比早期融合有一致且统计显著的提升。研究仅在MIMIC-CXR-JPG上内部评估，跨机构泛化性仍待验证，源代码已公开。
正文节选
Integrating Unimodal and Vision–Language Representations in Latent Space for Multi-Label Chest X-Ray Classification Abstract Multi-label chest X-ray classification has attracted considerable attention in recent years, with the effective use of visual representations and clinical semantic knowledge playing an important role. This study proposes a framework that combines unimodal representations from RAD-DINO with vision–language representations from BioViL-T for the classification of 14 labels in