探索用于医学图像中像素级受保护健康信息检测的基于AI的系统设计
Exploring AI-based System Design for Pixel-level Protected Health Information Detection in Medical Images
AI总结：
本研究提出了一个基于AI的医学图像PHI检测流水线，包含文本检测、提取和分析三个模块，通过基准测试YOLOv11、EasyOCR和GPT-4o，发现为各模块配置专门模型能实现性能、延迟和成本的最佳平衡，且LLMs的应用有效增强了OCR任务和端到端检测。
摘要：
医学图像的去标识化是在研究和临床环境中进行数据共享时确保隐私的关键步骤。该过程的初始步骤涉及检测受保护健康信息（PHI），这些信息可能存在于图像元数据中或印在图像像素内。尽管此类系统非常重要，但对现有基于AI的解决方案的评估仍然有限，这为开发可靠且稳健的工具造成了障碍。在本研究中，我们提出了一个基于AI的PHI检测流水线，包含三个关键模块：文本检测、文本提取和文本分析。我们在对应这些模块的不同设置下对三个模型——YOLOv11、EasyOCR和GPT-4o——进行了基准测试，评估它们在包含多种成像模态和PHI类别的两个不同数据集上的性能。我们的研究结果表明，最佳设置是为每个模块利用专门的视觉和语言模型，这在使用大型语言模型（LLMs）相关的性能、延迟和成本之间实现了令人赞赏的平衡。此外，我们展示了LLMs的应用不仅涉及识别PHI内容，还增强了OCR任务，并促进了端到端的PHI检测流水线，通过我们的分析展示了有前景的成果。
英文摘要：
De-identification of medical images is a critical step to ensure privacy during data sharing in research and clinical settings. The initial step in this process involves detecting Protected Health Information (PHI), which can be found in image metadata or imprinted within image pixels. Despite the importance of such systems, there has been limited evaluation of existing AI-based solutions, creating barriers to the development of reliable and robust tools. In this study, we present an AI-based pipeline for PHI detection, comprising three key modules: text detection, text extraction, and text analysis. We benchmark three models - YOLOv11, EasyOCR, and GPT-4o - across different setups corresponding to these modules, evaluating their performance on two different datasets encompassing multiple imaging modalities and PHI categories. Our findings indicate that the optimal setup involves utilizing dedicated vision and language models for each module, which achieves a commendable balance in performance, latency, and cost associated with the usage of Large Language Models (LLMs). Additionally, we show that the application of LLMs not only involves identifying PHI content but also enhances OCR tasks and facilitates an end-to-end PHI detection pipeline, showcasing promising outcomes through our analysis.
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。
