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Integrating Unimodal and Vision-Language Representations in Latent Space for Multi-Label Chest X-Ray Classification
Not provided
multimodalchest X-rayclassificationlatent space
2609.09185
Builder Relevance
1d ago70%
Abstract
This study proposes a framework that combines unimodal and vision-language representations for improved multi-label chest X-ray classification.
Reality Card
Core Claim
The proposed framework achieves a mean AUROC of 0.840 and an mAP of 0.467 by effectively integrating RAD-DINO and BioViL-T representations.
Method / Result
The best-performing model achieves a mean AUROC of 0.840.
Limitations
The study has only been evaluated internally on MIMIC-CXR-JPG, raising concerns about generalizability to other healthcare data.
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