Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

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 (doi: 10.20944/preprints202204.0219.v1). Wang, G.; Yang, Q.; Cong, W.; Donna, D. Preliminary Landscape Analysis of Deep Tomographic Imaging Patents. Preprints 2022, 2022040219 (doi: 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

MATHEMATICS & COMPUTER SCIENCE, Numerical Analysis & Optimization

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