← Search

Pablo Messina

2 accepted papers

2026

CURE: Curriculum-guided Multi-task Training for Reliable Anatomy Grounded Report Generation

CVPR 2026

Medical vision-language models can automate the generation of radiology reports but struggle with accurate visual grounding and factual consistency. Existing models often misalign textual findings with visual evidence, leading to unreliable or weakly grounded predictions. We present "CURE", an error

Cited by 1SourcecodeScholar
2024

Extracting and Encoding: Leveraging Large Language Models and Medical Knowledge to Enhance Radiological Text Representation

ACL 2024findings

Advancing representation learning in specialized fields like medicine remains challenging due to the scarcity of expert annotations for text and images. To tackle this issue, we present a novel two-stage framework designed to extract high-quality factual statements from free-text radiology reports i…