| The diversity–innovation paradox in science B Hofstra, VV Kulkarni, S Munoz-Najar Galvez, B He, D Jurafsky, ... Proceedings of the National Academy of Sciences 117 (17), 9284-9291, 2020 | 849 | 2020 |
| Video-based AI for beat-to-beat assessment of cardiac function D Ouyang, B He, A Ghorbani, N Yuan, J Ebinger, CP Langlotz, ... Nature 580 (7802), 252-256, 2020 | 660* | 2020 |
| Deep learning interpretation of echocardiograms A Ghorbani, D Ouyang, A Abid, B He, JH Chen, RA Harrington, DH Liang, ... npg Digital Medicine 3, 2020 | 354 | 2020 |
| Integrating spatial gene expression and breast tumour morphology via deep learning B He, L Bergenstråhle, L Stenbeck, A Abid, A Andersson, Å Borg, ... Nature biomedical engineering 4 (8), 827-834, 2020 | 271 | 2020 |
| Learning the structure of generative models without labeled data SH Bach, B He, A Ratner, C Ré International Conference on Machine Learning, 2017 | 184 | 2017 |
| Super-resolved spatial transcriptomics by deep data fusion L Bergenstråhle, B He, J Bergenstråhle, X Abalo, R Mirzazadeh, K Thrane, ... Nature biotechnology 40 (4), 476-479, 2021 | 102 | 2021 |
| High-throughput precision phenotyping of left ventricular hypertrophy with cardiovascular deep learning G Duffy, PP Cheng, N Yuan, B He, AC Kwan, MJ Shun-Shin, ... JAMA cardiology 7 (4), 386-395, 2022 | 100 | 2022 |
| Accelerated stochastic power iteration P Xu, B He, C De Sa, I Mitliagkas, C Re International Conference on Artificial Intelligence and Statistics, 58-67, 2018 | 89 | 2018 |
| Blinded, randomized trial of sonographer versus AI cardiac function assessment B He, AC Kwan, JH Cho, N Yuan, C Pollick, T Shiota, J Ebinger, NA Bello, ... Nature 616 (7957), 520-524, 2023 | 88 | 2023 |
| Inferring generative model structure with static analysis P Varma, BD He, P Bajaj, N Khandwala, I Banerjee, D Rubin, C Ré Advances in Neural Information Processing Systems 30, 2017 | 61 | 2017 |
| Deep learning evaluation of biomarkers from echocardiogram videos JW Hughes, N Yuan, B He, J Ouyang, J Ebinger, P Botting, J Lee, ... EBioMedicine 73, 2021 | 45 | 2021 |
| Scan order in Gibbs sampling: Models in which it matters and bounds on how much BD He, CM De Sa, I Mitliagkas, C Ré Advances in Neural Information Processing Systems 29, 2016 | 42 | 2016 |
| Socratic learning: Augmenting generative models to incorporate latent subsets in training data P Varma, B He, D Iter, P Xu, R Yu, C De Sa, C Ré arXiv preprint arXiv:1610.08123, 2016 | 37* | 2016 |
| How to evaluate deep learning for cancer diagnostics–factors and recommendations R Daneshjou, B He, D Ouyang, JY Zou Biochimica et Biophysica Acta (BBA) - Reviews on Cancer 1875 (2), 2021 | 33 | 2021 |
| Dynamic analysis of naive adaptive brain-machine interfaces KC Kowalski, BD He, L Srinivasan Neural Computation 25 (9), 2373-2420, 2013 | 23 | 2013 |
| Confounders mediate AI prediction of demographics in medical imaging G Duffy, SL Clarke, M Christensen, B He, N Yuan, S Cheng, D Ouyang NPJ digital medicine 5 (1), 188, 2022 | 22 | 2022 |
| Video-based deep learning for automated assessment of left ventricular ejection fraction in pediatric patients CD Reddy, L Lopez, D Ouyang, JY Zou, B He Journal of the American Society of Echocardiography 36 (5), 482-489, 2023 | 21 | 2023 |
| A simple optimal binary representation of mosaic floorplans and Baxter permutations BD He Theoretical Computer Science 532, 40-50, 2014 | 20* | 2014 |
| Systematic quantification of sources of variation in ejection fraction calculation using deep learning N Yuan, I Jain, N Rattehalli, B He, C Pollick, D Liang, P Heidenreich, ... Cardiovascular Imaging 14 (11), 2260-2262, 2021 | 18 | 2021 |
| Electrocardiographic deep learning for predicting post-procedural mortality D Ouyang, J Theurer, NR Stein, JW Hughes, P Elias, B He, N Yuan, ... arXiv preprint arXiv:2205.03242, 2022 | 11 | 2022 |
