KIKI‐net: cross‐domain convolutional neural networks for reconstructing undersampled magnetic resonance images

304 indexed citations

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This paper, published in 2018, received 304 indexed citations. Written by Taejoon Eo, Yohan Jun, Taeseong Kim, Jinseong Jang, Ho‐Joon Lee and Dosik Hwang covering the research area of Radiology, Nuclear Medicine and Imaging and Biomedical Engineering. It is primarily cited by scholars working on Radiology, Nuclear Medicine and Imaging (277 citations), Biomedical Engineering (81 citations) and Computational Mechanics (58 citations). Published in Magnetic Resonance in Medicine.

Countries where authors are citing KIKI‐net: cross‐domain convolutional neural networks for reconstructing undersampled magnetic resonance images

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This map shows the geographic impact of KIKI‐net: cross‐domain convolutional neural networks for reconstructing undersampled magnetic resonance images. 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 KIKI‐net: cross‐domain convolutional neural networks for reconstructing undersampled magnetic resonance images with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites KIKI‐net: cross‐domain convolutional neural networks for reconstructing undersampled magnetic resonance images more than expected).

Fields of papers citing KIKI‐net: cross‐domain convolutional neural networks for reconstructing undersampled magnetic resonance images

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Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of KIKI‐net: cross‐domain convolutional neural networks for reconstructing undersampled magnetic resonance images. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the KIKI‐net: cross‐domain convolutional neural networks for reconstructing undersampled magnetic resonance images.

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This paper is also available at doi.org/10.1002/mrm.27201.

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