Preliminary landscape analysis of deep tomographic imaging patents

Visual Computing for Industry, Biomedicine, and Art

Review

Donna L. Lizotte¹  Ge Wang²  Wenxiang Cong²  Qingsong Yang² 

[1] Murtha Cullina LLP, 02110, Boston, MA, USA;Rensselaer Polytechnic Institute, 12180, Troy, NY, USA;

关键词: Artificial intelligence;    Machine learning;    Deep learning;    Medical imaging;    Tomography;    Image reconstruction;   

DOI  :  10.1186/s42492-023-00130-x

 received in 2022-08-04, accepted in 2023-01-10,  发布年份 2023

来源: Springer

【 摘 要 】

Over recent years, the importance of the patent literature has become increasingly 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.

【 授权许可】

CC BY   
© The Author(s) 2023

【 图 表 】

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Fig. 43

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