CURE: Curriculum-guided Multi-task Training for Reliable Anatomy Grounded Report Generation
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866918299372093440 |
|---|---|
| author | Messina, Pablo Villa, Andrés Alcázar, Juan León Sánchez, Karen Hinojosa, Carlos Parra, Denis Soto, Álvaro Ghanem, Bernard |
| author_facet | Messina, Pablo Villa, Andrés Alcázar, Juan León Sánchez, Karen Hinojosa, Carlos Parra, Denis Soto, Álvaro Ghanem, Bernard |
| contents | 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-aware curriculum learning framework that improves grounding and report quality without any additional data. CURE fine-tunes a multimodal instructional model on phrase grounding, grounded report generation, and anatomy-grounded report generation using public datasets. The method dynamically adjusts sampling based on model performance, emphasizing harder samples to improve spatial and textual alignment. CURE improves grounding accuracy by +0.37 IoU, boosts report quality by +0.188 CXRFEScore, and reduces hallucinations by 18.6%. CURE is a data-efficient framework that enhances both grounding accuracy and report reliability. Code is available at https://github.com/PabloMessina/CURE and model weights at https://huggingface.co/pamessina/medgemma-4b-it-cure |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_15408 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | CURE: Curriculum-guided Multi-task Training for Reliable Anatomy Grounded Report Generation Messina, Pablo Villa, Andrés Alcázar, Juan León Sánchez, Karen Hinojosa, Carlos Parra, Denis Soto, Álvaro Ghanem, Bernard Computer Vision and Pattern Recognition Artificial Intelligence Computation and Language Machine Learning 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-aware curriculum learning framework that improves grounding and report quality without any additional data. CURE fine-tunes a multimodal instructional model on phrase grounding, grounded report generation, and anatomy-grounded report generation using public datasets. The method dynamically adjusts sampling based on model performance, emphasizing harder samples to improve spatial and textual alignment. CURE improves grounding accuracy by +0.37 IoU, boosts report quality by +0.188 CXRFEScore, and reduces hallucinations by 18.6%. CURE is a data-efficient framework that enhances both grounding accuracy and report reliability. Code is available at https://github.com/PabloMessina/CURE and model weights at https://huggingface.co/pamessina/medgemma-4b-it-cure |
| title | CURE: Curriculum-guided Multi-task Training for Reliable Anatomy Grounded Report Generation |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2601.15408 |