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List of co-cited articles
4 articles co-cited >1



Times Cited
  Times     Co-cited
Similarity


Assessment of MRI technologists in acceptance and willingness to integrate artificial intelligence into practice.
M M Abuzaid, H O Tekin, M Reza, I R Elhag, W Elshami. Radiography (Lond) 2021
9
33



Overview of artificial intelligence-based applications in radiotherapy: Recommendations for implementation and quality assurance.
Liesbeth Vandewinckele, Michaël Claessens, Anna Dinkla, Charlotte Brouwer, Wouter Crijns, Dirk Verellen, Wouter van Elmpt. Radiother Oncol 2020
61
33

Artificial intelligence in radiography: Where are we now and what does the future hold?
C Malamateniou, K M Knapp, M Pergola, N Woznitza, M Hardy. Radiography (Lond) 2021
5
20



The College of Radiographers Research Strategy for the next five years.
R Strudwick, M Harris, H McAlinney, P Plant, P Shuttleworth, J Woodley, R Harris, T O'Regan. Radiography (Lond) 2021
2
50




Clinical academic radiographers - A challenging but rewarding career.
I C Simcock, R Reeve, C Burnett, C Costigan, H McNair, C Robinson, O J Arthurs. Radiography (Lond) 2021
4
25


2
50

"It's what's behind the mask": Psychological diversity in compassionate patient care.
J Bleiker, K M Knapp, S Morgan-Trimmer, S J Hopkins. Radiography (Lond) 2018
10
16


Deep learning.
Yann LeCun, Yoshua Bengio, Geoffrey Hinton. Nature 2015
16

Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning.
Daniel S Kermany, Michael Goldbaum, Wenjia Cai, Carolina C S Valentim, Huiying Liang, Sally L Baxter, Alex McKeown, Ge Yang, Xiaokang Wu, Fangbing Yan,[...]. Cell 2018
963
16

Artificial Intelligence for MR Image Reconstruction: An Overview for Clinicians.
Dana J Lin, Patricia M Johnson, Florian Knoll, Yvonne W Lui. J Magn Reson Imaging 2021
32
16

GANs for medical image analysis.
Salome Kazeminia, Christoph Baur, Arjan Kuijper, Bram van Ginneken, Nassir Navab, Shadi Albarqouni, Anirban Mukhopadhyay. Artif Intell Med 2020
50
16

Generating synthetic CTs from magnetic resonance images using generative adversarial networks.
Hajar Emami, Ming Dong, Siamak P Nejad-Davarani, Carri K Glide-Hurst. Med Phys 2018
113
16

Deep Generative Adversarial Neural Networks for Compressive Sensing MRI.
Morteza Mardani, Enhao Gong, Joseph Y Cheng, Shreyas S Vasanawala, Greg Zaharchuk, Lei Xing, John M Pauly. IEEE Trans Med Imaging 2019
137
16

Clinically applicable deep learning for diagnosis and referral in retinal disease.
Jeffrey De Fauw, Joseph R Ledsam, Bernardino Romera-Paredes, Stanislav Nikolov, Nenad Tomasev, Sam Blackwell, Harry Askham, Xavier Glorot, Brendan O'Donoghue, Daniel Visentin,[...]. Nat Med 2018
757
16


Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets.
Stephanie A Harmon, Thomas H Sanford, Sheng Xu, Evrim B Turkbey, Holger Roth, Ziyue Xu, Dong Yang, Andriy Myronenko, Victoria Anderson, Amel Amalou,[...]. Nat Commun 2020
184
16

Undersampled MR image reconstruction using an enhanced recursive residual network.
Lijun Bao, Fuze Ye, Congbo Cai, Jian Wu, Kun Zeng, Peter C M van Zijl, Zhong Chen. J Magn Reson 2019
11
16

MR and CT data with multiobserver delineations of organs in the pelvic area-Part of the Gold Atlas project.
Tufve Nyholm, Stina Svensson, Sebastian Andersson, Joakim Jonsson, Maja Sohlin, Christian Gustafsson, Elisabeth Kjellén, Karin Söderström, Per Albertsson, Lennart Blomqvist,[...]. Med Phys 2018
17
16


Value of diffusion-weighted magnetic resonance imaging on the follow-up of nasopharyngeal carcinoma after radiotherapy.
Ji-Fei Xu, Xing-Wang Wu, Wan-Qin Wang, Ling-Ling Kong, Li-Ming Wu, Fan Wang. J Xray Sci Technol 2014
11
16

Staging of nasopharyngeal carcinoma investigated by magnetic resonance imaging.
Jin-Cheng Lu, Bao-Qing Wei, Wen-Zhan Chen, Pu-Dong Qian, Yi-Qin Zhang, Qing Wei, Wen-Wu Cha, Feng Li, Ming Ni. Radiother Oncol 2006
17
16

Can neural network able to estimate the prognosis of epilepsy patients according to risk factors?
Kezban Aslan, Hacer Bozdemir, Cenk Sahin, S Noyan Ogulata. J Med Syst 2010
8
16

An interpretable machine learning prognostic system for locoregionally advanced nasopharyngeal carcinoma based on tumor burden features.
Xi Chen, Yingxue Li, Xiang Li, Xun Cao, Yanqun Xiang, Weixiong Xia, Jianpeng Li, Mingyong Gao, Yuyao Sun, Kuiyuan Liu,[...]. Oral Oncol 2021
7
16

Parotid perfusion in nasopharyngeal carcinoma patients in early-to-intermediate stage after low-dose intensity-modulated radiotherapy: evaluated by fat-saturated dynamic contrast-enhanced magnetic resonance imaging.
Cheng-Chieh Cheng, Su-Chin Chiu, Yee-Min Jen, Hing-Chiu Chang, Hsiao-Wen Chung, Yi-Jui Liu, Hui-Chu Chiu, Cheng-Yu Chen, Guo-Shu Huang, Chun-Jung Juan. Magn Reson Imaging 2013
9
16

Analysis of nasopharyngeal carcinoma risk factors with Bayesian networks.
Alex Aussem, Sérgio Rodrigues de Morais, Marilys Corbex. Artif Intell Med 2012
9
16

Method of regulatory network that can explore protein regulations for disease classification.
Hong Qiang Wang, Hai Long Zhu, William C S Cho, Timothy T C Yip, Roger K C Ngan, Stephen C K Law. Artif Intell Med 2010
5
20

Magnetic resonance imaging of swallowing-related structures in nasopharyngeal carcinoma patients receiving IMRT: Longitudinal dose-response characterization of quantitative signal kinetics.
Jay A Messer, Abdallah S R Mohamed, Katherine A Hutcheson, Yao Ding, Jan S Lewin, Jihong Wang, Stephen Y Lai, Steven J Frank, Adam S Garden, Vlad Sandulache,[...]. Radiother Oncol 2016
17
16


Future of Radiotherapy in Nasopharyngeal Carcinoma.
Xue-Song Sun, Xiao-Yun Li, Qiu-Yan Chen, Lin-Quan Tang, Hai-Qiang Mai. Br J Radiol 2019
44
16





A novel use for routine CBCT imaging during radiotherapy to detect COVID-19.
A Clough, J Sanders, K Banfill, C Faivre-Finn, G Price, C L Eccles, M C Aznar, M Van Herk. Radiography (Lond) 2022
3
33

Radiographer-led online image guided adaptive radiotherapy: A qualitative investigation of the therapeutic radiographer role.
H A McNair, E Joyce, G O'Gara, M Jackson, B Peet, R A Huddart, T Wiseman. Radiography (Lond) 2021
9
16



Online training resources to aid therapeutic radiographers in engaging in conversations about physical activity and diet: A mixed methods study.
N D Pallin, J Webb, L Brown, N Woznitza, A Stewart-Lord, L Charlesworth, R J Beeken, A Fisher. Radiography (Lond) 2022
4
25


Gender diversity in therapeutic radiography: A mixed methods exploration of the gender influences impacting on male students' career choices.
J Nightingale, R Appleyard, J McNamara, M Panchbhaya, J Posnett, J Stone. Radiography (Lond) 2022
1
100

Introducing magnetic resonance imaging into the lung cancer radiotherapy workflow - An assessment of patient experience.
S Bellhouse, S Brown, M Dubec, S Taylor, R Hales, L Whiteside, J Yorke, C Faivre-Finn. Radiography (Lond) 2021
5
20


Co-cited is the co-citation frequency, indicating how many articles cite the article together with the query article. Similarity is the co-citation as percentage of the times cited of the query article or the article in the search results, whichever is the lowest. These numbers are calculated for the last 100 citations when articles are cited more than 100 times.