{"title":"基于深度学习的实时医学视频去噪:在血管造影中的应用。","authors":"Praneeth Sadda, Taha Qarni","doi":"10.5120/ijais2018451755","DOIUrl":null,"url":null,"abstract":"<p><p>This paper describes the design, training, and evaluation of a deep neural network for removing noise from medical fluoroscopy videos. The method described in this work, unlike the current standard techniques for video denoising, is able to deliver a result quickly enough to be used in real-time scenarios. Furthermore, this method is able to produce results of a similar quality to the existing industry-standard denoising techniques.</p>","PeriodicalId":92376,"journal":{"name":"International journal of applied information systems","volume":"12 13","pages":"22-28"},"PeriodicalIF":0.0000,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.5120/ijais2018451755","citationCount":"10","resultStr":"{\"title\":\"Real-Time Medical Video Denoising with Deep Learning: Application to Angiography.\",\"authors\":\"Praneeth Sadda, Taha Qarni\",\"doi\":\"10.5120/ijais2018451755\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>This paper describes the design, training, and evaluation of a deep neural network for removing noise from medical fluoroscopy videos. The method described in this work, unlike the current standard techniques for video denoising, is able to deliver a result quickly enough to be used in real-time scenarios. Furthermore, this method is able to produce results of a similar quality to the existing industry-standard denoising techniques.</p>\",\"PeriodicalId\":92376,\"journal\":{\"name\":\"International journal of applied information systems\",\"volume\":\"12 13\",\"pages\":\"22-28\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.5120/ijais2018451755\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International journal of applied information systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5120/ijais2018451755\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International journal of applied information systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5120/ijais2018451755","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Real-Time Medical Video Denoising with Deep Learning: Application to Angiography.
This paper describes the design, training, and evaluation of a deep neural network for removing noise from medical fluoroscopy videos. The method described in this work, unlike the current standard techniques for video denoising, is able to deliver a result quickly enough to be used in real-time scenarios. Furthermore, this method is able to produce results of a similar quality to the existing industry-standard denoising techniques.