[需引用] 医学影像会议中的数据使用与引用实践
[Citation needed] Data usage and citation practices in medical imaging conferences
AI总结：
针对医学影像领域数据集引用无统一标准、使用情况难追踪的问题，研究开发两款开源工具，分析两大会议十年间20个公开数据集的使用与引用特征，揭示使用集中化与引用习惯差异的现状。
摘要：
医学影像论文往往聚焦于方法论，但算法的质量与结论的有效性高度依赖所使用的数据集。由于创建数据集需要大量投入，研究人员常使用公开可用的数据集，然而科学论文中引用所用数据集尚无公认标准，导致数据集使用情况难以追踪。本研究中，我们提出了两款自主开发的可助力检测数据集使用情况的开源工具：一款是基于OpenAlex与全文分析的流水线工具，其仓库地址为https://github.com/TheoSourget/Public_Medical_Datasets_References；另一款是PDF标注软件，地址为https://github.com/TheoSourget/pdf_annotator，本研究中用它来手动标注论文中是否出现数据集。我们将两款工具应用于一项研究，分析MICCAI与MIDL会议论文中20个公开医学数据集的使用情况。我们统计了2013年至2023年间，数据集在论文中三类出现形式的占比及变化趋势：被引用、在全文中被提及、既被引用又被提及。研究结果表明，数据集的使用集中在有限的几个集合上。我们还发现了不同的引用习惯，这使得追踪工作的自动化存在难度。
英文摘要：
Medical imaging papers often focus on methodology, but the quality of the algorithms and the validity of the conclusions are highly dependent on the datasets used. As creating datasets requires a lot of effort, researchers often use publicly available datasets, there is however no adopted standard for citing the datasets used in scientific papers, leading to difficulty in tracking dataset usage. In this work, we present two open-source tools we created that could help with the detection of dataset usage, a pipeline \url{https://github.com/TheoSourget/Public_Medical_Datasets_References} using OpenAlex and full-text analysis, and a PDF annotation software \url{https://github.com/TheoSourget/pdf_annotator} used in our study to manually label the presence of datasets. We applied both tools on a study of the usage of 20 publicly available medical datasets in papers from MICCAI and MIDL. We compute the proportion and the evolution between 2013 and 2023 of 3 types of presence in a paper: cited, mentioned in the full text, cited and mentioned. Our findings demonstrate the concentration of the usage of a limited set of datasets. We also highlight different citing practices, making the automation of tracking difficult.
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。
