| Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets SA Harmon, TH Sanford, S Xu, EB Turkbey, H Roth, Z Xu, D Yang, ... Nature communications 11 (1), 4080, 2020 | 561 | 2020 |
| Federated learning for predicting clinical outcomes in patients with COVID-19 I Dayan, HR Roth, A Zhong, A Harouni, A Gentili, AZ Abidin, A Liu, ... Nature medicine 27 (10), 1735-1743, 2021 | 437 | 2021 |
| Generalizing deep learning for medical image segmentation to unseen domains via deep stacked transformation L Zhang, X Wang, D Yang, T Sanford, S Harmon, B Turkbey, BJ Wood, ... IEEE transactions on medical imaging 39 (7), 2531-2540, 2020 | 350 | 2020 |
| Federated semi-supervised learning for COVID region segmentation in chest CT using multi-national data from China, Italy, Japan D Yang, Z Xu, W Li, A Myronenko, HR Roth, S Harmon, S Xu, B Turkbey, ... Medical image analysis 70, 101992, 2021 | 219 | 2021 |
| A magnetic resonance imaging–based prediction model for prostate biopsy risk stratification S Mehralivand, JH Shih, S Rais-Bahrami, A Oto, S Bednarova, JW Nix, ... JAMA oncology 4 (5), 678-685, 2018 | 165 | 2018 |
| A grading system for the assessment of risk of extraprostatic extension of prostate cancer at multiparametric MRI S Mehralivand, JH Shih, S Harmon, C Smith, J Bloom, M Czarniecki, ... Radiology 290 (3), 709-719, 2019 | 162 | 2019 |
| Federated learning improves site performance in multicenter deep learning without data sharing KV Sarma, S Harmon, T Sanford, HR Roth, Z Xu, J Tetreault, D Xu, ... Journal of the American Medical Informatics Association 28 (6), 1259-1264, 2021 | 142 | 2021 |
| Intra‐and interreader reproducibility of PI‐RADSv2: A multireader study CP Smith, SA Harmon, T Barrett, LK Bittencourt, YM Law, H Shebel, JY An, ... Journal of Magnetic Resonance Imaging 49 (6), 1694-1703, 2019 | 121 | 2019 |
| Clinical impact of PSMA-based 18F–DCFBC PET/CT imaging in patients with biochemically recurrent prostate cancer after primary local therapy E Mena, ML Lindenberg, JH Shih, S Adler, S Harmon, E Bergvall, D Citrin, ... European journal of nuclear medicine and molecular imaging 45, 4-11, 2018 | 80 | 2018 |
| Can computer-aided diagnosis assist in the identification of prostate cancer on prostate MRI? a multi-center, multi-reader investigation S Gaur, N Lay, SA Harmon, S Doddakashi, S Mehralivand, B Argun, ... Oncotarget 9 (73), 33804, 2018 | 78 | 2018 |
| Artificial intelligence at the intersection of pathology and radiology in prostate cancer SA Harmon, S Tuncer, T Sanford, PL Choyke, B Türkbey Diagnostic and Interventional Radiology 25 (3), 183, 2019 | 77 | 2019 |
| Repeatability of quantitative 18F-NaF PET: a multicenter study C Lin, T Bradshaw, T Perk, S Harmon, J Eickhoff, N Jallow, PL Choyke, ... Journal of nuclear medicine 57 (12), 1872-1879, 2016 | 76 | 2016 |
| Multi-domain image completion for random missing input data L Shen, W Zhu, X Wang, L Xing, JM Pauly, B Turkbey, SA Harmon, ... IEEE transactions on medical imaging 40 (4), 1113-1122, 2020 | 65 | 2020 |
| Diagnostic classification of solitary pulmonary nodules using dual time 18F-FDG PET/CT image texture features in granuloma-endemic regions S Chen, S Harmon, T Perk, X Li, M Chen, Y Li, R Jeraj Scientific reports 7 (1), 9370, 2017 | 65 | 2017 |
| Quick guide on radiology image pre-processing for deep learning applications in prostate cancer research S Masoudi, SA Harmon, S Mehralivand, SM Walker, H Raviprakash, ... Journal of Medical Imaging 8 (1), 010901-010901, 2021 | 63 | 2021 |
| Deep‐learning‐based artificial intelligence for PI‐RADS classification to assist multiparametric prostate MRI interpretation: a development study T Sanford, SA Harmon, EB Turkbey, D Kesani, S Tuncer, M Madariaga, ... Journal of Magnetic Resonance Imaging 52 (5), 1499-1507, 2020 | 63 | 2020 |
| Determination of disease severity in COVID-19 patients using deep learning in chest X-ray images M Blain, MT Kassin, N Varble, X Wang, Z Xu, D Xu, G Carrafiello, ... Diagnostic and interventional radiology 27 (1), 20, 2021 | 58 | 2021 |
| Quality of prostate MRI: is the PI-RADS standard sufficient? J Sackett, JH Shih, SE Reese, JR Brender, SA Harmon, T Barrett, ... Academic radiology 28 (2), 199-207, 2021 | 57 | 2021 |
| Quantitative assessment of early [18F] sodium fluoride positron emission tomography/computed tomography response to treatment in men with metastatic prostate cancer to bone SA Harmon, T Perk, C Lin, J Eickhoff, PL Choyke, WL Dahut, AB Apolo, ... Journal of Clinical Oncology 35 (24), 2829, 2017 | 57 | 2017 |
| Multiresolution application of artificial intelligence in digital pathology for prediction of positive lymph nodes from primary tumors in bladder cancer SA Harmon, TH Sanford, GT Brown, C Yang, S Mehralivand, JM Jacob, ... JCO clinical cancer informatics 4, 367-382, 2020 | 55 | 2020 |
