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Preliminary Landscape Analysis of Deep Tomographic Imaging Patents
Version 1
: Received: 24 April 2022 / Approved: 25 April 2022 / Online: 25 April 2022 (05:35:17 CEST)
How to cite: Wang, G.; Yang, Q.; Cong, W.; Donna, D. Preliminary Landscape Analysis of Deep Tomographic Imaging Patents. Preprints 2022, 2022040219. https://doi.org/10.20944/preprints202204.0219.v1 Wang, G.; Yang, Q.; Cong, W.; Donna, D. Preliminary Landscape Analysis of Deep Tomographic Imaging Patents. Preprints 2022, 2022040219. https://doi.org/10.20944/preprints202204.0219.v1
Abstract
Over recent years, the importance of patent literature has become more recognized in the academic setting. In the context of artificial intelligence, deep learning, and data sciences, patents are relevant to not only industry but also academe and other communities. In this article, we focus on deep tomographic imaging and perform a preliminary landscape analysis of the related patent literature. Our search tool is PatSeer . Our patent bibliometric data is summarized in various figures and tables. In particular, we qualitatively analyze key deep tomographic patent literature.
Keywords
Artificial intelligence (AI), machine learning; deep learning; medical imaging; tomography; image reconstruction
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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