| Current applications and future impact of machine learning in radiology G Choy, O Khalilzadeh, M Michalski, S Do, AE Samir, OS Pianykh, ... Radiology 288 (2), 318-328, 2018 | 638 | 2018 |
| Artificial intelligence and machine learning in radiology: opportunities, challenges, pitfalls, and criteria for success JH Thrall, X Li, Q Li, C Cruz, S Do, K Dreyer, J Brink Journal of the American College of Radiology 15 (3), 504-508, 2018 | 556 | 2018 |
| Abdominal CT: comparison of adaptive statistical iterative and filtered back projection reconstruction techniques S Singh, MK Kalra, J Hsieh, PE Licato, S Do, HH Pien, MA Blake Radiology 257 (2), 373-383, 2010 | 534 | 2010 |
| Fully automated deep learning system for bone age assessment H Lee, S Tajmir, J Lee, M Zissen, BA Yeshiwas, TK Alkasab, G Choy, ... Journal of digital imaging 30, 427-441, 2017 | 417 | 2017 |
| How much data is needed to train a medical image deep learning system to achieve necessary high accuracy? J Cho, K Lee, E Shin, G Choy, S Do arXiv preprint arXiv:1511.06348, 2015 | 373 | 2015 |
| An explainable deep-learning algorithm for the detection of acute intracranial haemorrhage from small datasets H Lee, S Yune, M Mansouri, M Kim, SH Tajmir, CE Guerrier, SA Ebert, ... Nature biomedical engineering 3 (3), 173-182, 2019 | 364 | 2019 |
| Diffuse lung disease: CT of the chest with adaptive statistical iterative reconstruction technique P Prakash, MK Kalra, JB Ackman, SR Digumarthy, J Hsieh, S Do, ... Radiology 256 (1), 261-269, 2010 | 195 | 2010 |
| Deep convolutional neural network–based software improves radiologist detection of malignant lung nodules on chest radiographs Y Sim, MJ Chung, E Kotter, S Yune, M Kim, S Do, K Han, H Kim, S Yang, ... Radiology 294 (1), 199-209, 2020 | 173 | 2020 |
| Comparison of hybrid and pure iterative reconstruction techniques with conventional filtered back projection: dose reduction potential in the abdomen S Singh, MK Kalra, S Do, JB Thibault, H Pien, OOJ Connor, MA Blake Journal of computer assisted tomography 36 (3), 347-353, 2012 | 162 | 2012 |
| Pixel-level deep segmentation: artificial intelligence quantifies muscle on computed tomography for body morphometric analysis H Lee, FM Troschel, S Tajmir, G Fuchs, J Mario, FJ Fintelmann, S Do Journal of digital imaging 30, 487-498, 2017 | 155 | 2017 |
| Coronary artery plaques: cardiac CT with model-based and adaptive-statistical iterative reconstruction technique H Scheffel, P Stolzmann, CL Schlett, LC Engel, GP Major, M Károlyi, S Do, ... European journal of radiology 81 (3), e363-e369, 2012 | 140 | 2012 |
| Artificial intelligence-assisted interpretation of bone age radiographs improves accuracy and decreases variability SH Tajmir, H Lee, R Shailam, HI Gale, JC Nguyen, SJ Westra, R Lim, ... Skeletal radiology 48, 275-283, 2019 | 93 | 2019 |
| Sinogram-affirmed iterative reconstruction of low-dose chest CT: effect on image quality and radiation dose MK Kalra, M Woisetschläger, N Dahlström, S Singh, S Digumarthy, S Do, ... American Journal of Roentgenology 201 (2), W235-W244, 2013 | 92 | 2013 |
| Histogram analysis of lipid-core plaques in coronary computed tomographic angiography: ex vivo validation against histology CL Schlett, P Maurovich-Horvat, M Ferencik, H Alkadhi, P Stolzmann, ... Investigative Radiology 48 (9), 646-653, 2013 | 81 | 2013 |
| Multi GPU implementation of iterative tomographic reconstruction algorithms B Jang, D Kaeli, S Do, H Pien 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro …, 2009 | 81 | 2009 |
| Basics of deep learning: a radiologist's guide to understanding published radiology articles on deep learning S Do, KD Song, JW Chung Korean journal of radiology 21 (1), 33-41, 2020 | 80 | 2020 |
| Computed Tomography (CT) of the Chest at Less Than 1 mSv: An Ongoing Prospective Clinical Trial of Chest CT at Submillisievert Radiation Doses with Iterative Model Image … RDA Khawaja, S Singh, M Gilman, A Sharma, S Do, S Pourjabbar, ... Journal of computer assisted tomography 38 (4), 613-619, 2014 | 76 | 2014 |
| Urinary stone detection on CT images using deep convolutional neural networks: evaluation of model performance and generalization A Parakh, H Lee, JH Lee, BH Eisner, DV Sahani, S Do Radiology: Artificial Intelligence 1 (4), e180066, 2019 | 71 | 2019 |
| Submillisievert chest CT with filtered back projection and iterative reconstruction techniques A Padole, S Singh, JB Ackman, C Wu, S Do, S Pourjabbar, RDA Khawaja, ... American Journal of Roentgenology 203 (4), 772-781, 2014 | 61 | 2014 |
| Ultra-low dose abdominal MDCT: using a knowledge-based Iterative Model Reconstruction technique for substantial dose reduction in a prospective clinical study RDA Khawaja, S Singh, M Blake, M Harisinghani, G Choy, ... European journal of radiology 84 (1), 2-10, 2015 | 60 | 2015 |
Shahein TajmirMassachusetts General Hospital, Harvard Medical SchoolVerified email at mgh.harvard.edu
