标题： 基于临床指导的数据合成用于喉部病变检测 显示英文标题

标题： Clinically-guided Data Synthesis for Laryngeal Lesion Detection

摘要： 尽管计算机辅助诊断（CADx）和检测（CADe）系统在各个医学领域取得了显著进展，但它们在耳鼻喉科等专业领域中的应用仍然有限。 在后者中，当前的评估方法高度依赖于操作者的专业知识，病变的高异质性使诊断变得复杂，尽管活检仍然是金标准，但其成本和风险都很高。 对于专业的内窥镜CADx/e系统来说，一个关键瓶颈是缺乏具有足够变异性以实现现实世界泛化的良好标注数据集。 本研究介绍了一种新方法，该方法利用潜在扩散模型（LDM）结合ControlNet适配器，根据临床观察生成喉内窥镜图像-标注对。 该方法通过将扩散过程条件化，生成具有现实感、高质量和临床相关性的图像特征，从而解决数据稀缺问题，这些特征能够捕捉多种解剖状况。 所提出的方法可以用于扩展CADx/e模型的训练数据集，增强喉科学的评估过程。 事实上，在下游检测任务中，仅添加10%的合成数据就使模型内部测试时的喉部病变检测率提高了9%，在跨域外部数据上的检测率提高了22.1%。 此外，通过让5名不同专业水平的耳鼻喉科专家对合成图像与真实图像的区分信心进行评分，评估了生成图像的真实性。 这项工作有望加速喉部疾病诊断自动化工具的发展，提供一种解决数据稀缺问题的方案，并展示了合成数据在现实场景中的适用性。 显示英文摘要

摘要： Although computer-aided diagnosis (CADx) and detection (CADe) systems have made significant progress in various medical domains, their application is still limited in specialized fields such as otorhinolaryngology. In the latter, current assessment methods heavily depend on operator expertise, and the high heterogeneity of lesions complicates diagnosis, with biopsy persisting as the gold standard despite its substantial costs and risks. A critical bottleneck for specialized endoscopic CADx/e systems is the lack of well-annotated datasets with sufficient variability for real-world generalization. This study introduces a novel approach that exploits a Latent Diffusion Model (LDM) coupled with a ControlNet adapter to generate laryngeal endoscopic image-annotation pairs, guided by clinical observations. The method addresses data scarcity by conditioning the diffusion process to produce realistic, high-quality, and clinically relevant image features that capture diverse anatomical conditions. The proposed approach can be leveraged to expand training datasets for CADx/e models, empowering the assessment process in laryngology. Indeed, during a downstream task of detection, the addition of only 10% synthetic data improved the detection rate of laryngeal lesions by 9% when the model was internally tested and 22.1% on out-of-domain external data. Additionally, the realism of the generated images was evaluated by asking 5 expert otorhinolaryngologists with varying expertise to rate their confidence in distinguishing synthetic from real images. This work has the potential to accelerate the development of automated tools for laryngeal disease diagnosis, offering a solution to data scarcity and demonstrating the applicability of synthetic data in real-world scenarios.