Preliminary Landscape Analysis of Deep Tomographic Imaging Patents

Ge Wang; Qingsong Yang; Wenxiang Cong; Donna Donna

doi:10.20944/preprints202204.0219.v1

Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 24 April 2022 / Approved: 25 April 2022 / Online: 25 April 2022 (05:35:17 CEST)

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.

Artificial intelligence (AI), machine learning; deep learning; medical imaging; tomography; image reconstruction

Computer Science and Mathematics, Computer Vision and Graphics

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Preliminary Landscape Analysis of Deep Tomographic Imaging Patents

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