| Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study Q Dou, TY So, M Jiang, Q Liu, V Vardhanabhuti, G Kaissis, Z Li, W Si, ... NPJ digital medicine 4 (1), 60, 2021 | 285 | 2021 |
| DRONE: Dual-domain residual-based optimization network for sparse-view CT reconstruction W Wu, D Hu, C Niu, H Yu, V Vardhanabhuti, G Wang IEEE Transactions on Medical Imaging 40 (11), 3002-3014, 2021 | 261 | 2021 |
| Multi-granularity cross-modal alignment for generalized medical visual representation learning F Wang, Y Zhou, S Wang, V Vardhanabhuti, L Yu Advances in neural information processing systems 35, 33536-33549, 2022 | 258 | 2022 |
| USPSTF2013 versus PLCOm2012 lung cancer screening eligibility criteria (International Lung Screening Trial): interim analysis of a prospective cohort study MC Tammemägi, M Ruparel, A Tremblay, R Myers, J Mayo, J Yee, ... The Lancet Oncology 23 (1), 138-148, 2022 | 175 | 2022 |
| Assessment of intratumoral and peritumoral computed tomography radiomics for predicting pathological complete response to neoadjuvant chemoradiation in patients with esophageal … Y Hu, C Xie, H Yang, JWK Ho, J Wen, L Han, KWH Chiu, J Fu, ... JAMA network open 3 (9), e2015927-e2015927, 2020 | 160 | 2020 |
| Lung cancer screening in Asia: an expert consensus report DCL Lam, CK Liam, S Andarini, S Park, DSW Tan, N Singh, SH Jang, ... Journal of Thoracic Oncology 18 (10), 1303-1322, 2023 | 142 | 2023 |
| Deep learning in breast cancer imaging: A decade of progress and future directions L Luo, X Wang, Y Lin, X Ma, A Tan, R Chan, V Vardhanabhuti, WCW Chu, ... IEEE Reviews in Biomedical Engineering, 2024 | 138 | 2024 |
| Computed tomography-based deep-learning prediction of neoadjuvant chemoradiotherapy treatment response in esophageal squamous cell carcinoma Y Hu, C Xie, H Yang, JWK Ho, J Wen, L Han, KO Lam, IYH Wong, ... Radiotherapy and Oncology 154, 6-13, 2021 | 135 | 2021 |
| Image quality assessment of standard-and low-dose chest CT using filtered back projection, adaptive statistical iterative reconstruction, and novel model-based iterative … V Vardhanabhuti, RJ Loader, GR Mitchell, RD Riordan, CA Roobottom American journal of Roentgenology 200 (3), 545-552, 2013 | 134 | 2013 |
| Early embryonic light detection improves survival TK Tamai, V Vardhanabhuti, NS Foulkes, D Whitmore Current Biology 14 (3), R104-R105, 2004 | 122 | 2004 |
| Effect of machine learning re-sampling techniques for imbalanced datasets in 18F-FDG PET-based radiomics model on prognostication performance in cohorts of … C Xie, R Du, JWK Ho, HH Pang, KWH Chiu, EYP Lee, V Vardhanabhuti European journal of nuclear medicine and molecular imaging 47 (12), 2826-2835, 2020 | 95 | 2020 |
| Image comparative assessment using iterative reconstructions: clinical comparison of low-dose abdominal/pelvic computed tomography between adaptive statistical, model-based … V Vardhanabhuti, RD Riordan, GR Mitchell, C Hyde, CA Roobottom Investigative radiology 49 (4), 209-216, 2014 | 91 | 2014 |
| Recommendations for accurate CT diagnosis of suspected acute aortic syndrome (AAS)—on behalf of the British Society of Cardiovascular Imaging (BSCI)/British Society of … V Vardhanabhuti, E Nicol, G Morgan-Hughes, CA Roobottom, G Roditi, ... The British Journal of Radiology 89 (1061), 20150705, 2016 | 84 | 2016 |
| Extra-pulmonary manifestations of sarcoidosis V Vardhanabhuti, N Venkatanarasimha, G Bhatnagar, M Maviki, S Iyengar, ... Clinical radiology 67 (3), 263-276, 2012 | 80 | 2012 |
| Stabilizing deep tomographic reconstruction: Part A. Hybrid framework and experimental results W Wu, D Hu, W Cong, H Shan, S Wang, C Niu, P Yan, H Yu, ... Patterns 3 (5), 2022 | 77 | 2022 |
| Deep learning based spectral CT imaging W Wu, D Hu, C Niu, L Vanden Broeke, APH Butler, P Cao, J Atlas, ... Neural Networks 144, 342-358, 2021 | 73 | 2021 |
| Radiomics model to predict early progression of nonmetastatic nasopharyngeal carcinoma after intensity modulation radiation therapy: a multicenter study R Du, VH Lee, H Yuan, KO Lam, HH Pang, Y Chen, EY Lam, PL Khong, ... Radiology: Artificial Intelligence 1 (4), e180075, 2019 | 65 | 2019 |
| Deep learning-based thigh muscle segmentation for reproducible fat fraction quantification using fat–water decomposition MRI J Ding, P Cao, HC Chang, Y Gao, SHS Chan, V Vardhanabhuti Insights into imaging 11 (1), 128, 2020 | 56 | 2020 |
| Comparison of image quality between filtered back-projection and the adaptive statistical and novel model-based iterative reconstruction techniques in abdominal CT for renal … V Vardhanabhuti, S Ilyas, C Gutteridge, SJ Freeman, CA Roobottom Insights into imaging 4 (5), 661-669, 2013 | 55 | 2013 |
| Assessment of image quality on effects of varying tube voltage and automatic tube current modulation with hybrid and pure iterative reconstruction techniques in abdominal … V Vardhanabhuti, R Loader, CA Roobottom Investigative radiology 48 (3), 167-174, 2013 | 52 | 2013 |
Christopher HydeProfessor of Public Health and Clinical EpidemiologyVerified email at exeter.ac.uk
