{"title":"深度学习和高性能计算促进生物医学健康应用(DeepHealth)","authors":"Mónica Caballero, J. A. Gómez, Aimilia Bantouna","doi":"10.1109/CBMS.2019.00040","DOIUrl":null,"url":null,"abstract":"This document introduces the DeepHealth project: \"Deep-Learning and HPC to Boost Biomedical Applications for Health\". This project is funded by the European Commission under the H2020 framework program and aims to reduce the gap between the availability of mature enough AI-solutions and their deployment in real scenarios. Several existing software platforms provided by industrial partners will integrate state-of-the-art machine-learning algorithms and will be used for giving support to doctors in diagnosis, increasing their capabilities and efficiency. The DeepHealth consortium is composed by 21 partners from 9 European countries including hospitals, universities, large industry and SMEs.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Deep-Learning and HPC to Boost Biomedical Applications for Health (DeepHealth)\",\"authors\":\"Mónica Caballero, J. A. Gómez, Aimilia Bantouna\",\"doi\":\"10.1109/CBMS.2019.00040\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This document introduces the DeepHealth project: \\\"Deep-Learning and HPC to Boost Biomedical Applications for Health\\\". This project is funded by the European Commission under the H2020 framework program and aims to reduce the gap between the availability of mature enough AI-solutions and their deployment in real scenarios. Several existing software platforms provided by industrial partners will integrate state-of-the-art machine-learning algorithms and will be used for giving support to doctors in diagnosis, increasing their capabilities and efficiency. The DeepHealth consortium is composed by 21 partners from 9 European countries including hospitals, universities, large industry and SMEs.\",\"PeriodicalId\":311634,\"journal\":{\"name\":\"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)\",\"volume\":\"36 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CBMS.2019.00040\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CBMS.2019.00040","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Deep-Learning and HPC to Boost Biomedical Applications for Health (DeepHealth)
This document introduces the DeepHealth project: "Deep-Learning and HPC to Boost Biomedical Applications for Health". This project is funded by the European Commission under the H2020 framework program and aims to reduce the gap between the availability of mature enough AI-solutions and their deployment in real scenarios. Several existing software platforms provided by industrial partners will integrate state-of-the-art machine-learning algorithms and will be used for giving support to doctors in diagnosis, increasing their capabilities and efficiency. The DeepHealth consortium is composed by 21 partners from 9 European countries including hospitals, universities, large industry and SMEs.