Automatic multiorgan segmentation in thorax CT images using U‐net‐GAN

226 indexed citations
published 2019

Countries where authors are citing Automatic multiorgan segmentation in thorax CT images using U‐net‐GAN

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Citations

This map shows the geographic impact of Automatic multiorgan segmentation in thorax CT images using U‐net‐GAN. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Automatic multiorgan segmentation in thorax CT images using U‐net‐GAN with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Automatic multiorgan segmentation in thorax CT images using U‐net‐GAN more than expected).

Fields of papers citing Automatic multiorgan segmentation in thorax CT images using U‐net‐GAN

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Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of Automatic multiorgan segmentation in thorax CT images using U‐net‐GAN. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Automatic multiorgan segmentation in thorax CT images using U‐net‐GAN.

About Automatic multiorgan segmentation in thorax CT images using U‐net‐GAN

This paper, published in 2019, received 226 indexed citations . Written by Xue Dong, Yang Lei, Tonghe Wang, M.A. Thomas, Walter J. Curran, Tian Liu and Xiaofeng Yang covering the research area of Radiation. It is primarily cited by scholars working on Radiology, Nuclear Medicine and Imaging (115 citations), Computer Vision and Pattern Recognition (53 citations), Artificial Intelligence (52 citations), Radiation (51 citations) and Biomedical Engineering (44 citations). Published in Medical Physics.

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This paper is also available at doi.org/10.1002/mp.13458.

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