Ultrasound-QBench：大语言模型能否助力超声成像质量评估？
Ultrasound-QBench: Can LLMs Aid in Quality Assessment of Ultrasound Imaging?
AI总结：
本文提出 Ultrasound-QBench 基准，基于 IVUSQA 与 CardiacUltraQA 从分类、评分和比较三维度评估 MLLM 的超声图像质量评估能力。
摘要：
随着超声检查量的急剧增加，由于操作者熟练度和成像环境的差异，低质量超声成像逐渐增多，给诊断准确性带来严重负担，在危重病例中甚至会导致重新启动诊断的风险。为帮助临床医生选择高质量超声图像并确保准确诊断，我们推出 Ultrasound-QBench，这是一个在超声图像质量评估任务上系统评估多模态大语言模型（MLLM）的综合基准。Ultrasound-QBench 建立了两个从不同来源收集的数据集：包含 7,709 张图像的 IVUSQA，以及包含 3,863 张图像的 CardiacUltraQA。这些涵盖常见超声成像伪影的图像由专业超声专家标注，并被划分为高、中、低三个质量等级。为更好地评估 MLLM，我们将质量评估任务分解为三个维度：定性分类、定量评分和比较评估。对 7 个开源 MLLM 以及 1 个专有 MLLM 的评估表明，MLLM 在超声图像质量分类的低级视觉任务方面具备初步能力。我们希望该基准能激励研究界更深入地发掘并增强 MLLM 在医学成像任务中尚未开发的潜力。
英文摘要：
With the dramatic upsurge in the volume of ultrasound examinations, low-quality ultrasound imaging has gradually increased due to variations in operator proficiency and imaging circumstances, imposing a severe burden on diagnosis accuracy and even entailing the risk of restarting the diagnosis in critical cases. To assist clinicians in selecting high-quality ultrasound images and ensuring accurate diagnoses, we introduce Ultrasound-QBench, a comprehensive benchmark that systematically evaluates multimodal large language models (MLLMs) on quality assessment tasks of ultrasound images. Ultrasound-QBench establishes two datasets collected from diverse sources: IVUSQA, consisting of 7,709 images, and CardiacUltraQA, containing 3,863 images. These images encompassing common ultrasound imaging artifacts are annotated by professional ultrasound experts and classified into three quality levels: high, medium, and low. To better evaluate MLLMs, we decompose the quality assessment task into three dimensionalities: qualitative classification, quantitative scoring, and comparative assessment. The evaluation of 7 open-source MLLMs as well as 1 proprietary MLLMs demonstrates that MLLMs possess preliminary capabilities for low-level visual tasks in ultrasound image quality classification. We hope this benchmark will inspire the research community to delve deeper into uncovering and enhancing the untapped potential of MLLMs for medical imaging tasks.
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。
