{"title":"Cohort Selection through Content-Based Image Retrieval: vfM A Case Study","authors":"Mayank Agarwal, Javed Mostafa","doi":"10.1109/HISB.2012.42","DOIUrl":null,"url":null,"abstract":"In this paper, we propose ViewFinder Medicine (vfM) for automatically identifying cohort classes for MRI scans. It involves predicting a cohort class for the heretofore unseen patient (and related images) and offering linkages to historical diagnosis data associated with the members of the predicted cohort class. The basic idea is to offer a relatively accurate cohort class for a new patient so that the cohort can be used as a baseline to understand current patient's status and develop a treatment plan.","PeriodicalId":375089,"journal":{"name":"2012 IEEE Second International Conference on Healthcare Informatics, Imaging and Systems Biology","volume":"43 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE Second International Conference on Healthcare Informatics, Imaging and Systems Biology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/HISB.2012.42","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In this paper, we propose ViewFinder Medicine (vfM) for automatically identifying cohort classes for MRI scans. It involves predicting a cohort class for the heretofore unseen patient (and related images) and offering linkages to historical diagnosis data associated with the members of the predicted cohort class. The basic idea is to offer a relatively accurate cohort class for a new patient so that the cohort can be used as a baseline to understand current patient's status and develop a treatment plan.
在本文中，我们提出了ViewFinder Medicine (vfM)来自动识别MRI扫描的队列类别。它包括预测迄今为止未见过的患者(和相关图像)的队列类别，并提供与预测队列类别成员相关的历史诊断数据的链接。其基本思路是为新患者提供一个相对准确的队列分类，以便该队列可以作为了解当前患者状态和制定治疗计划的基线。
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