Yaniv Ziv, Faruque Jessica, Howe Sally, Dunn Kathel, Sharlip David, Bond Andrew, Perillan Pablo, Bodenreider Olivier, Ackerman Michael J, Yoo Terry S
National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.
TAJ Technologies Inc., Mendota Heights, MN, USA.
IEEE Appl Imag Pattern Recognit Workshop. 2016 Oct;2016. doi: 10.1109/AIPR.2016.8010584. Epub 2017 Aug 17.
In January 2016 the U.S. National Library of Medicine announced a challenge competition calling for the development and discovery of high-quality algorithms and software that rank how well consumer images of prescription pills match reference images of pills in its authoritative RxIMAGE collection. This challenge was motivated by the need to easily identify unknown prescription pills both by healthcare personnel and the general public. Potential benefits of this capability include confirmation of the pill in settings where the documentation and medication have been separated, such as in a disaster or emergency; and confirmation of a pill when the prescribed medication changes from brand to generic, or for any other reason the shape and color of the pill change. The data for the competition consisted of two types of images, high quality macro photographs, reference images, and consumer quality photographs of the quality we expect users of a proposed application to acquire. A training dataset consisting of 2000 reference images and 5000 corresponding consumer quality images acquired from 1000 pills was provided to challenge participants. A second dataset acquired from 1000 pills with similar distributions of shape and color was reserved as a segregated testing set. Challenge submissions were required to produce a ranking of the reference images, given a consumer quality image as input. Determination of the winning teams was done using the mean average precision quality metric, with the three winners obtaining mean average precision scores of 0.27, 0.09, and 0.08. In the retrieval results, the correct image was amongst the top five ranked images 43%, 12%, and 11% of the time, out of 5000 query/consumer images. This is an initial promising step towards development of an NLM software system and application-programming interface facilitating pill identification. The training dataset will continue to be freely available online at: http://pir.nlm.nih.gov/challenge/submission.html.
2016年1月，美国国立医学图书馆宣布举办一场挑战赛，征集高质量算法和软件，用于对消费者拍摄的处方药丸图像与该馆权威的RxIMAGE数据库中的药丸参考图像进行匹配排名。发起这项挑战是因为医疗人员和普通公众都需要轻松识别未知的处方药丸。这项功能的潜在好处包括：在文件记录与药品分离的情况下（如灾难或紧急情况）确认药丸；在处方药从品牌药换成仿制药，或因任何其他原因药丸形状和颜色发生变化时确认药丸。竞赛数据包括两种图像：高质量的微距照片（参考图像），以及我们预期拟议应用程序用户获取的消费者质量照片。向参赛人员提供了一个训练数据集，其中包含从1000颗药丸获取的2000张参考图像和5000张相应的消费者质量图像。从1000颗形状和颜色分布相似的药丸获取的第二个数据集被保留作为单独的测试集。要求参赛作品以消费者质量图像作为输入，对参考图像进行排名。使用平均精度质量指标来确定获胜团队，三位获胜者的平均精度得分分别为0.27、0.09和0.08。在检索结果中，在5000个查询/消费者图像中，正确图像在排名前五的图像中的出现概率分别为43%、12%和11%。这是朝着开发美国国立医学图书馆软件系统和便于药丸识别的应用程序编程接口迈出的初步有前景的一步。训练数据集将继续在以下网址免费在线获取：http://pir.nlm.nih.gov/challenge/submission.html。
