{"title":"基于医学图像的患者识别方法及其对患者隐私和开放医疗数据的影响","authors":"Laura Carolina Martínez Esmeral, A. Uhl","doi":"10.1109/CBMS55023.2022.00079","DOIUrl":null,"url":null,"abstract":"In this paper, we provide an overview of techniques for human subject identification from biomedical signals, highlighting the potential threat for patient privacy considering public repositories of medical data. After an in-depth review of lesser known approaches, we conclude that performing a disentanglement and elimination of the identity related attributes from the medical image data is a potential solution for this problem.","PeriodicalId":218475,"journal":{"name":"2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Patient identification methods based on medical imagery and their impact on patient privacy and open medical data\",\"authors\":\"Laura Carolina Martínez Esmeral, A. Uhl\",\"doi\":\"10.1109/CBMS55023.2022.00079\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we provide an overview of techniques for human subject identification from biomedical signals, highlighting the potential threat for patient privacy considering public repositories of medical data. After an in-depth review of lesser known approaches, we conclude that performing a disentanglement and elimination of the identity related attributes from the medical image data is a potential solution for this problem.\",\"PeriodicalId\":218475,\"journal\":{\"name\":\"2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS)\",\"volume\":\"55 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CBMS55023.2022.00079\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CBMS55023.2022.00079","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Patient identification methods based on medical imagery and their impact on patient privacy and open medical data
In this paper, we provide an overview of techniques for human subject identification from biomedical signals, highlighting the potential threat for patient privacy considering public repositories of medical data. After an in-depth review of lesser known approaches, we conclude that performing a disentanglement and elimination of the identity related attributes from the medical image data is a potential solution for this problem.