使用 LangChain 生成乳腺超声报告
Breast Ultrasound Report Generation using LangChain
AI总结：
本文提出基于 LangChain 与 LLM 集成多种图像分析工具，从多张乳腺超声图像提取特征并自动生成标准化临床报告，实验和临床评估验证了其有效性。
摘要：
乳腺超声（BUS）是乳腺成像领域的关键诊断工具，有助于早期发现和表征乳腺异常。解读乳腺超声图像通常需要创建全面的医学报告，其中包含用于及时评估患者病情的重要信息。然而，超声成像系统需要采集不同部位的多张图像才能汇编成一份报告，这带来了耗时的挑战。为解决这一问题，我们提出在乳腺报告生成流程中，通过使用大语言模型（LLM）的 LangChain 集成多种图像分析工具。通过将指定工具与 LangChain 的文本生成相结合，我们的方法能够准确提取超声图像中的相关特征，在临床语境中对其进行解释，并生成全面、标准化的报告。该方法不仅减轻了放射科医生和医疗专业人员的负担，还提高了报告的一致性和质量。大量实验表明，所提方法涉及的每种工具都能提供具有定性和定量意义的结果。此外，对生成报告的临床评估表明，所提方法能够以具有临床意义的方式生成报告。
英文摘要：
Breast ultrasound (BUS) is a critical diagnostic tool in the field of breast imaging, aiding in the early detection and characterization of breast abnormalities. Interpreting breast ultrasound images commonly involves creating comprehensive medical reports, containing vital information to promptly assess the patient's condition. However, the ultrasound imaging system necessitates capturing multiple images of various parts to compile a single report, presenting a time-consuming challenge. To address this problem, we propose the integration of multiple image analysis tools through a LangChain using Large Language Models (LLM), into the breast reporting process. Through a combination of designated tools and text generation through LangChain, our method can accurately extract relevant features from ultrasound images, interpret them in a clinical context, and produce comprehensive and standardized reports. This approach not only reduces the burden on radiologists and healthcare professionals but also enhances the consistency and quality of reports. The extensive experiments shows that each tools involved in the proposed method can offer qualitatively and quantitatively significant results. Furthermore, clinical evaluation on the generated reports demonstrates that the proposed method can make report in clinically meaningful way.
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。
