IEEE Access Special Section Editorial: Emerging Deep Learning Theories and Methods for Biomedical Engineering
The accelerating power of deep learning in diagnosing a disease and analyzing medical data will empower physicians and speed up decision-making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated large amounts of biomedical informati...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2021-01-01
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| Series: | IEEE Access |
| Online Access: | https://ieeexplore.ieee.org/document/9440029/ |
| _version_ | 1818647770561511424 |
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| author | Yu-Dong Zhang Zhengchao Dong Juan Manuel Gorriz Yizhang Jiang Ming Yang Shui-Hua Wang |
| author_facet | Yu-Dong Zhang Zhengchao Dong Juan Manuel Gorriz Yizhang Jiang Ming Yang Shui-Hua Wang |
| author_sort | Yu-Dong Zhang |
| collection | DOAJ |
| description | The accelerating power of deep learning in diagnosing a disease and analyzing medical data will empower physicians and speed up decision-making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated large amounts of biomedical information in recent years. These pose challenges, demands, and opportunities for new AI methods and computational models for efficient data processing, analysis, and modeling with the generated data that are important for clinical applications and in understanding the underlying biological process. |
| first_indexed | 2024-12-17T01:07:49Z |
| format | Article |
| id | doaj.art-3194a9f2940c485e8e2ea96667ec0b7c |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T01:07:49Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-3194a9f2940c485e8e2ea96667ec0b7c2022-12-21T22:09:13ZengIEEEIEEE Access2169-35362021-01-019740387404310.1109/ACCESS.2021.30803559440029IEEE Access Special Section Editorial: Emerging Deep Learning Theories and Methods for Biomedical EngineeringYu-Dong Zhang0https://orcid.org/0000-0002-4870-1493Zhengchao Dong1Juan Manuel Gorriz2Yizhang Jiang3Ming Yang4Shui-Hua Wang5School of Informatics, University of Leicester, Leicester, U.K.Molecular Imaging and Neuropathology Division, Columbia University, New York, NY, USADepartment of Signal Theory, Networking and Communications, University of Granada, Granada, SpainSchool of Digital Media, Jiangnan University, Wuxi, ChinaDepartment of Radiology, Nanjing Medical University, Nanjing, ChinaSchool of Architecture Building and Civil Engineering, Loughborough University, Loughborough, U.K.The accelerating power of deep learning in diagnosing a disease and analyzing medical data will empower physicians and speed up decision-making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated large amounts of biomedical information in recent years. These pose challenges, demands, and opportunities for new AI methods and computational models for efficient data processing, analysis, and modeling with the generated data that are important for clinical applications and in understanding the underlying biological process.https://ieeexplore.ieee.org/document/9440029/ |
| spellingShingle | Yu-Dong Zhang Zhengchao Dong Juan Manuel Gorriz Yizhang Jiang Ming Yang Shui-Hua Wang IEEE Access Special Section Editorial: Emerging Deep Learning Theories and Methods for Biomedical Engineering IEEE Access |
| title | IEEE Access Special Section Editorial: Emerging Deep Learning Theories and Methods for Biomedical Engineering |
| title_full | IEEE Access Special Section Editorial: Emerging Deep Learning Theories and Methods for Biomedical Engineering |
| title_fullStr | IEEE Access Special Section Editorial: Emerging Deep Learning Theories and Methods for Biomedical Engineering |
| title_full_unstemmed | IEEE Access Special Section Editorial: Emerging Deep Learning Theories and Methods for Biomedical Engineering |
| title_short | IEEE Access Special Section Editorial: Emerging Deep Learning Theories and Methods for Biomedical Engineering |
| title_sort | ieee access special section editorial emerging deep learning theories and methods for biomedical engineering |
| url | https://ieeexplore.ieee.org/document/9440029/ |
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