| Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning P Rajpurkar, J Irvin, K Zhu, B Yang, H Mehta, T Duan, D Ding, A Bagul, ... arXiv preprint arXiv:1711.05225, 2017 | 2904 | 2017 |
| 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 | 2299 | 2019 |
| 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 | 1087 | 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 | 598 | 2018 |
| Mura: Large dataset for abnormality detection in musculoskeletal radiographs P Rajpurkar, J Irvin, A Bagul, D Ding, T Duan, H Mehta, B Yang, K Zhu, ... arXiv preprint arXiv:1712.06957, 2017 | 379 | 2017 |
| 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 | 208 | 2019 |
| Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning. arXiv P Rajpurkar, J Irvin, K Zhu, B Yang, H Mehta, T Duan, D Ding, A Bagul, ... arXiv preprint arXiv:1711.05225, 2017 | 164 | 2017 |
| CheXNet: radiologist-level pneumonia detection on chest X-rays with deep learning. 2017 P Rajpurkar, J Irvin, K Zhu, B Yang, H Mehta, T Duan, D Ding, A Bagul, ... arXiv preprint arXiv:1711.05225, 2020 | 128 | 2020 |
| Sex differences in cognitive decline in subjects with high likelihood of mild cognitive impairment due to Alzheimer’s disease D Sohn, K Shpanskaya, JE Lucas, JR Petrella, AJ Saykin, RE Tanzi, ... Scientific reports 8 (1), 7490, 2018 | 120 | 2018 |
| PENet—a scalable deep-learning model for automated diagnosis of pulmonary embolism using volumetric CT imaging SC Huang, T Kothari, I Banerjee, C Chute, RL Ball, N Borus, A Huang, ... NPJ digital medicine 3 (1), 61, 2020 | 117* | 2020 |
| MR imaging–based radiomic signatures of distinct molecular subgroups of medulloblastoma M Iv, M Zhou, K Shpanskaya, S Perreault, Z Wang, E Tranvinh, ... American Journal of Neuroradiology 40 (1), 154-161, 2019 | 105 | 2019 |
| Automated abnormality detection in lower extremity radiographs using deep learning M Varma, M Lu, R Gardner, J Dunnmon, N Khandwala, P Rajpurkar, ... Nature Machine Intelligence 1 (12), 578-583, 2019 | 74 | 2019 |
| Mapping the effects of ApoE4, age and cognitive status on 18F-florbetapir PET measured regional cortical patterns of beta-amyloid density and growth KR Murphy, SM Landau, KR Choudhury, CA Hostage, KS Shpanskaya, ... Neuroimage 78, 474-480, 2013 | 54 | 2013 |
| Deep learning for pediatric posterior fossa tumor detection and classification: a multi-institutional study JL Quon, W Bala, LC Chen, J Wright, LH Kim, M Han, K Shpanskaya, ... American Journal of Neuroradiology 41 (9), 1718-1725, 2020 | 49 | 2020 |
| Educational attainment and hippocampal atrophy in the Alzheimer's disease neuroimaging initiative cohort KS Shpanskaya, KR Choudhury, C Hostage Jr, KR Murphy, JR Petrella, ... Journal of Neuroradiology 41 (5), 350-357, 2014 | 36 | 2014 |
| MRI radiogenomics of pediatric medulloblastoma: a multicenter study M Zhang, SW Wong, JN Wright, MW Wagner, S Toescu, M Han, LT Tam, ... Radiology 304 (2), 406-416, 2022 | 33 | 2022 |
| Attention-guided deep learning for gestational age prediction using fetal brain MRI L Shen, J Zheng, EH Lee, K Shpanskaya, ES McKenna, MG Atluri, ... Scientific reports 12 (1), 1408, 2022 | 25 | 2022 |
| Age-dependent white matter characteristics of the cerebellar peduncles from infancy through adolescence L Bruckert, K Shpanskaya, ES McKenna, LR Borchers, M Yablonski, ... The Cerebellum 18, 372-387, 2019 | 24 | 2019 |
| Impact of 18F-florbetapir PET imaging of β-amyloid neuritic plaque density on clinical decision-making AS Zannas, PM Doraiswamy, KS Shpanskaya, KR Murphy, JR Petrella, ... Neurocase 20 (4), 466-473, 2014 | 23 | 2014 |
| Deep learning for automated classification of inferior vena cava filter types on radiographs JC Ni, K Shpanskaya, M Han, EH Lee, BH Do, WT Kuo, KW Yeom, ... Journal of Vascular and Interventional Radiology 31 (1), 66-73, 2020 | 19 | 2020 |
