Conference paper Open Access

Deep-Learning and HPC to Boost Biomedical Applications for Health (DeepHealth)

Monica Caballero; Jon Ander Gómez; Aimilia Bantouna


Dublin Core Export

<?xml version='1.0' encoding='utf-8'?>
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:creator>Monica Caballero</dc:creator>
  <dc:creator>Jon Ander Gómez</dc:creator>
  <dc:creator>Aimilia Bantouna</dc:creator>
  <dc:date>2019-06-05</dc:date>
  <dc:description>The paper 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.

This document is an accepted paper published using the Green Open Access Model. Published paper available at https://www.computer.org/csdl/proceedings-article/cbms/2019/228600a150/1cdNXiHj5QY 

© 2019 IEEE.  Personal use of this material is permitted.  Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.”</dc:description>
  <dc:identifier>https://zenodo.org/record/3636402</dc:identifier>
  <dc:identifier>10.1109/CBMS.2019.00040</dc:identifier>
  <dc:identifier>oai:zenodo.org:3636402</dc:identifier>
  <dc:language>eng</dc:language>
  <dc:relation>info:eu-repo/grantAgreement/EC/H2020/825111/</dc:relation>
  <dc:relation>url:https://zenodo.org/communities/deephealth</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:source>150-155</dc:source>
  <dc:subject>libraries;europe;medical services;predictive models;medical diagnostic imaging;software</dc:subject>
  <dc:title>Deep-Learning and HPC to Boost Biomedical Applications for Health (DeepHealth)</dc:title>
  <dc:type>info:eu-repo/semantics/conferencePaper</dc:type>
  <dc:type>publication-conferencepaper</dc:type>
</oai_dc:dc>
233
198
views
downloads
Views 233
Downloads 198
Data volume 102.5 MB
Unique views 195
Unique downloads 194

Share

Cite as