跳到主要导航 跳到搜索 跳到主要内容

Grand Challenge on Respiratory Sound Classification for SPRSound Dataset

  • Qing Zhang
  • , Jing Zhang
  • , Jiajun Yuan
  • , Huajie Huang
  • , Yuhang Zhang
  • , Baoqin Zhang
  • , Gaomei Lv
  • , Shuzhu Lin
  • , Na Wang
  • , Xin Liu
  • , Mingyu Tang
  • , Yahua Wang
  • , Hui Ma
  • , Lu Liu
  • , Shuhua Yuan
  • , Hongyuan Zhou
  • , Jian Zhao
  • , Yongfu Li*
  • , Yong Yin*
  • , Liebin Zhao*
  • Guoxing Wang, Yong Lian
*此作品的通讯作者
  • Shanghai Jiao Tong University
  • Shanghai Engineering Research Center of Intelligence Pediatrics (SERCIP)
  • Shanghai University
  • Sanya Maternity and Child Care Hospital
  • The First People's Hospital of Taicang
  • Linyi City People Hospital
  • Fengcheng Hospital
  • Child Health Hospital
  • Harbin Medical University
  • Ltd.

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

摘要

It is important to continuously monitor our respiratory system to prevent us from suffering respiratory-releated diseases. This demands for an automatic respiratory sounds software to speed up diagnosis and to reduce the workload of physicians. In the IEEE BioCAS 2022 conference, we have organized the first grand challenge on respiratory sound classification using the paediatric respiratory sound (SPRSound). This event has invited 45 teams with more than 100 open source entries and the top 5 teams are invited to present their works in the IEEE BioCAS 2022.

源语言英语
主期刊名BioCAS 2022 - IEEE Biomedical Circuits and Systems Conference
主期刊副标题Intelligent Biomedical Systems for a Better Future, Proceedings
出版商Institute of Electrical and Electronics Engineers Inc.
页213-217
页数5
ISBN(电子版)9781665469173
DOI
出版状态已出版 - 2022
已对外发布是
活动2022 IEEE Biomedical Circuits and Systems Conference, BioCAS 2022 - Taipei, 中国台湾
期限: 13 10月 2022 → 15 10月 2022

丛书

姓名BioCAS 2022 - IEEE Biomedical Circuits and Systems Conference: Intelligent Biomedical Systems for a Better Future, Proceedings

会议

会议2022 IEEE Biomedical Circuits and Systems Conference, BioCAS 2022
国家/地区中国台湾
市Taipei
时期13/10/22 → 15/10/22

学术指纹

探究 'Grand Challenge on Respiratory Sound Classification for SPRSound Dataset' 的科研主题。它们共同构成独一无二的学术指纹。

引用此