Medizinische Universität Graz Austria/Österreich - Forschungsportal - Medical University of Graz
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SHR Neuro Krebs Kardio Lipid Stoffw Microb
Egger, J; Gsaxner, C; Pepe, A; Pomykala, KL; Jonske, F; Kurz, M; Li, JN; Kleesiek, J.
Medical deep learning-A systematic meta-review
COMPUT METH PROG BIO. 2022; 221: 106874
Doi: 10.1016/j.cmpb.2022.106874
Web of Science
PubMed
FullText
FullText_MUG
- Führende Autor*innen der Med Uni Graz
- Egger Jan
- Co-Autor*innen der Med Uni Graz
- Li Jianning
- Schwarz-Gsaxner Christina
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- Abstract:
- Deep learning has remarkably impacted several different scientific disciplines over the last few years. For example, in image processing and analysis, deep learning algorithms were able to outperform other cutting-edge methods. Additionally, deep learning has delivered state-of-the-art results in tasks like autonomous driving, outclassing previous attempts. There are even instances where deep learning outperformed humans, for example with object recognition and gaming. Deep learning is also showing vast potential in the medical domain. With the collection of large quantities of patient records and data, and a trend towards personalized treatments, there is a great need for automated and reliable processing and analysis of health information. Patient data is not only collected in clinical centers, like hospitals and private practices, but also by mobile healthcare apps or online websites. The abundance of collected patient data and the recent growth in the deep learning field has resulted in a large increase in research effort s. In Q2/2020, the search engine PubMed returned already over 11,0 0 0 results for the search term 'deep learning', and around 90% of these publications are from the last three years. However, even though PubMed represents the largest search engine in the medical field, it does not cover all medical-related publications. Hence, a complete overview of the field of 'medical deep learning' is almost impossible to obtain and acquiring a full overview of medical sub-fields is becoming increasingly more difficult. Nevertheless, several review and survey articles about medical deep learning have been published within the last few years. They focus, in general, on specific medical scenarios, like the analysis of medical images containing specific pathologies. With these surveys as a foundation, the aim of this article is to provide the first high-level, systematic meta-review of medical deep learning surveys. (c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )
- Find related publications in this database (Keywords)
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- Generative adversarial networks
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