首个可跨模态评估医学影像质量的通用大模型，提升诊断准确性。
MedIQA: A Scalable Foundation Model for Prompt-Driven Medical Image Quality Assessment
- 基于多模态数据构建通用医学影像质量评估模型
- 在多种临床场景下性能显著优于现有方法
- 适合医疗AI研发与临床质量控制人员使用
医学成像技术的快速发展对精准自动化图像质量评估（IQA）提出了迫切需求，以保障诊断准确性。现有方法难以在不同成像模态和临床场景中泛化。为此，我们提出MedIQA，首个面向医学IQA的综合性基础模型，可处理图像尺寸、模态、解剖区域及类型多样性。我们构建了大规模多模态数据集，包含大量人工标注的质量评分。模型集成显著切片评估模块，聚焦诊断相关区域；采用自动提示策略，实现上游物理参数预训练与下游专家标注微调的对齐。大量实验表明，MedIQA在多个下游任务中显著超越基线，建立了可扩展的医学IQA框架，推动诊疗流程与临床决策优化。
原文摘要 · Abstract (English)
Rapid advances in medical imaging technology underscore the critical need for precise and automated image quality assessment (IQA) to ensure diagnostic accuracy. Existing medical IQA methods, however, struggle to generalize across diverse modalities and clinical scenarios. In response, we introduce MedIQA, the first comprehensive foundation model for medical IQA, designed to handle variability in image dimensions, modalities, anatomical regions, and types. We developed a large-scale multi-modality dataset with plentiful manually annotated quality scores to support this. Our model integrates a salient slice assessment module to focus on diagnostically relevant regions feature retrieval and employs an automatic prompt strategy that aligns upstream physical parameter pre-training with downstream expert annotation fine-tuning. Extensive experiments demonstrate that MedIQA significantly outperforms baselines in multiple downstream tasks, establishing a scalable framework for medical IQA and advancing diagnostic workflows and clinical decision-making.
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