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Integrating Unimodal and Vision-Language Representations in Latent Space for Multi-Label Chest X-Ray Classification

arXiv · AI, language, vision and robotics · article · Aug 31, 2026 · UTC

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 the MIMIC-CXR-JPG dataset. The RAD-DINO and BioViL-T embeddings and their combined representation are refined separately in latent space before being normalized and fused across the three branches. In addition to improving

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