CURE: Curriculum-guided Multi-task Training for Reliable Anatomy Grounded Report Generation
This addresses the need for more reliable and grounded automated radiology reports for medical professionals, though it is incremental as it builds on existing multimodal instructional models with a novel training framework.
The paper tackled the problem of inaccurate visual grounding and factual consistency in medical vision-language models for radiology report generation, resulting in improvements of +0.37 IoU in grounding accuracy, +0.188 CXRFEScore in report quality, and an 18.6% reduction in hallucinations.
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