| Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison J Irvin, P Rajpurkar, M Ko, Y Yu, S Ciurea-Ilcus, C Chute, H Marklund, ... Proceedings of the AAAI conference on artificial intelligence 33 (01), 590-597, 2019 | 2971 | 2019 |
| Systematic literature review of imaging features of spinal degeneration in asymptomatic populations W Brinjikji, PH Luetmer, B Comstock, BW Bresnahan, LE Chen, RA Deyo, ... American journal of neuroradiology 36 (4), 811-816, 2015 | 1406 | 2015 |
| Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists P Rajpurkar, J Irvin, RL Ball, K Zhu, B Yang, H Mehta, T Duan, D Ding, ... PLoS medicine 15 (11), e1002686, 2018 | 1297 | 2018 |
| Deep-learning-assisted diagnosis for knee magnetic resonance imaging: development and retrospective validation of MRNet N Bien, P Rajpurkar, RL Ball, J Irvin, A Park, E Jones, M Bereket, BN Patel, ... PLoS medicine 15 (11), e1002699, 2018 | 695 | 2018 |
| Performance of a deep-learning neural network model in assessing skeletal maturity on pediatric hand radiographs DB Larson, MC Chen, MP Lungren, SS Halabi, NV Stence, CP Langlotz Radiology 287 (1), 313-322, 2018 | 468 | 2018 |
| The RSNA pediatric bone age machine learning challenge SS Halabi, LM Prevedello, J Kalpathy-Cramer, AB Mamonov, A Bilbily, ... Radiology 290 (2), 498-503, 2019 | 460 | 2019 |
| Augmenting the national institutes of health chest radiograph dataset with expert annotations of possible pneumonia G Shih, CC Wu, SS Halabi, MD Kohli, LM Prevedello, TS Cook, A Sharma, ... Radiology: Artificial Intelligence 1 (1), e180041, 2019 | 322 | 2019 |
| Deep learning–assisted diagnosis of cerebral aneurysms using the HeadXNet model A Park, C Chute, P Rajpurkar, J Lou, RL Ball, K Shpanskaya, ... JAMA network open 2 (6), e195600-e195600, 2019 | 253 | 2019 |
| Construction of a machine learning dataset through collaboration: the RSNA 2019 brain CT hemorrhage challenge AE Flanders, LM Prevedello, G Shih, SS Halabi, J Kalpathy-Cramer, ... Radiology: Artificial Intelligence 2 (3), e190211, 2020 | 216 | 2020 |
| Human–machine partnership with artificial intelligence for chest radiograph diagnosis BN Patel, L Rosenberg, G Willcox, D Baltaxe, M Lyons, J Irvin, ... NPJ digital medicine 2 (1), 111, 2019 | 210 | 2019 |
| Challenges related to artificial intelligence research in medical imaging and the importance of image analysis competitions LM Prevedello, SS Halabi, G Shih, CC Wu, MD Kohli, FH Chokshi, ... Radiology: Artificial Intelligence 1 (1), e180031, 2019 | 171 | 2019 |
| The RSNA pulmonary embolism CT dataset E Colak, FC Kitamura, SB Hobbs, CC Wu, MP Lungren, LM Prevedello, ... Radiology: Artificial Intelligence 3 (2), e200254, 2021 | 103 | 2021 |
| Decoding COVID-19 pneumonia: comparison of deep learning and radiomics CT image signatures H Wang, L Wang, EH Lee, J Zheng, W Zhang, S Halabi, C Liu, K Deng, ... European journal of nuclear medicine and molecular imaging 48, 1478-1486, 2021 | 98 | 2021 |
| Artificial swarm intelligence employed to amplify diagnostic accuracy in radiology L Rosenberg, M Lungren, S Halabi, G Willcox, D Baltaxe, M Lyons 2018 IEEE 9th annual information technology, electronics and mobile …, 2018 | 67 | 2018 |
| Artificial intelligence algorithm improves radiologist performance in skeletal age assessment: a prospective multicenter randomized controlled trial DK Eng, NB Khandwala, J Long, NR Fefferman, SV Lala, NA Strubel, ... Radiology 301 (3), 692-699, 2021 | 65 | 2021 |
| Data science: big data, machine learning, and artificial intelligence RC Carlos, CE Kahn, S Halabi Journal of the American College of Radiology 15 (3), 497-498, 2018 | 62 | 2018 |
| Improving automated pediatric bone age estimation using ensembles of models from the 2017 RSNA machine learning challenge I Pan, HH Thodberg, SS Halabi, J Kalpathy-Cramer, DB Larson Radiology: Artificial Intelligence 1 (6), e190053, 2019 | 53 | 2019 |
| The effect of including benchmark prevalence data of common imaging findings in spine image reports on health care utilization among adults undergoing spine imaging: a stepped … JG Jarvik, EN Meier, KT James, LS Gold, KW Tan, LG Kessler, P Suri, ... JAMA network open 3 (9), e2015713-e2015713, 2020 | 45 | 2020 |
| Lumbar Imaging With Reporting Of Epidemiology (LIRE)—protocol for a pragmatic cluster randomized trial JG Jarvik, BA Comstock, KT James, AL Avins, BW Bresnahan, RA Deyo, ... Contemporary clinical trials 45, 157-163, 2015 | 44 | 2015 |
| Technical challenges in the clinical application of radiomics FA Shaikh, BJ Kolowitz, O Awan, HJ Aerts, A von Reden, S Halabi, ... JCO clinical cancer informatics 1, 1-8, 2017 | 42 | 2017 |
Curtis P. LanglotzProfessor of Radiology, Medicine, and Biomedical Data Science, Stanford Universitystanford.edu의 이메일 확인됨
