CURE：基于课程引导的多任务训练实现可靠的解剖学接地报告生成
CURE: Curriculum-guided Multi-task Training for Reliable Anatomy Grounded Report Generation
AI总结：
提出CURE框架，通过课程学习动态调整多任务训练，提升医学报告生成的视觉接地准确性和事实一致性，无需额外数据。
摘要：
医学视觉语言模型可以自动生成放射学报告，但在精确的视觉接地和事实一致性方面存在困难。现有模型常常将文本发现与视觉证据错误对齐，导致不可靠或弱接地的预测。我们提出CURE，一个错误感知的课程学习框架，无需任何额外数据即可改善接地和报告质量。CURE在短语接地、接地报告生成和解剖学接地报告生成上，使用公共数据集微调多模态指令模型。该方法基于模型性能动态调整采样，强调困难样本以改善空间和文本对齐。CURE将接地准确率提高了+0.35 IoU，报告质量提高了+0.192 CXRFEScore，并将幻觉减少了18.6%。CURE是一个数据高效的框架，增强了接地准确性和报告可靠性。代码可从此https URL获取，模型权重可从此https URL获取。
英文摘要：
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 +0.35 IoU, boosts report quality by +0.192 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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
