On abdominal CT, a vision-language pre-training method using organ-level normal/abnormal contrastive learning and a VQ-VAE normality model achieves 84.9% average zero-shot AUC, beating prior methods by 3.6%.
Bootstrapping chest ct image understanding by distilling knowledge from x-ray expert models
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Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training
On abdominal CT, a vision-language pre-training method using organ-level normal/abnormal contrastive learning and a VQ-VAE normality model achieves 84.9% average zero-shot AUC, beating prior methods by 3.6%.