| 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 | 580 | 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 | 359* | 2020 |
| Deep learning interpretation of echocardiograms A Ghorbani, D Ouyang, A Abid, B He, JH Chen, RA Harrington, DH Liang, ... NPJ digital medicine 3 (1), 10, 2020 | 236 | 2020 |
| Integrating spatial gene expression and breast tumour morphology via deep learning B He, L Bergenstrĺhle, L Stenbeck, A Abid, A Andersson, Ĺ Borg, ... Nature biomedical engineering 4 (8), 827-834, 2020 | 164 | 2020 |
| Learning the structure of generative models without labeled data SH Bach, B He, A Ratner, C Ré International Conference on Machine Learning (ICML), 2017 | 145 | 2017 |
| 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 | 73 | 2018 |
| Super-resolved spatial transcriptomics by deep data fusion L Bergenstrĺhle, B He, J Bergenstrĺhle, X Abalo, R Mirzazadeh, K Thrane, ... Nature biotechnology 40 (4), 476-479, 2022 | 56 | 2022 |
| 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 | 49 | 2017 |
| 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 |
| 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 | 33 | 2016 |
| 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 | 32 | 2022 |
| Deep learning evaluation of biomarkers from echocardiogram videos JW Hughes, N Yuan, B He, J Ouyang, J Ebinger, P Botting, J Lee, ... EBioMedicine 73, 103613, 2021 | 25* | 2021 |
| Dynamic analysis of naive adaptive brain-machine interfaces KC Kowalski, BD He, L Srinivasan Neural Computation 25 (9), 2373-2420, 2013 | 23 | 2013 |
| 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), 188515, 2021 | 22 | 2021 |
| A simple optimal binary representation of mosaic floorplans and Baxter permutations BD He Theoretical Computer Science 532, 40-50, 2014 | 19* | 2014 |
| Signal quality of endovascular electroencephalography BD He, M Ebrahimi, L Palafox, L Srinivasan Journal of Neural Engineering 13 (1), 016016, 2016 | 11 | 2016 |
| 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 | 10 | 2021 |
| AI-enabled in silico immunohistochemical characterization for Alzheimer's disease B He, S Bukhari, E Fox, A Abid, J Shen, C Kawas, M Corrada, T Montine, ... Cell reports methods 2 (4), 100191, 2022 | 6 | 2022 |
| Generalized analog thresholding for spike acquisition at ultralow sampling rates BD He, A Wein, LR Varshney, J Kusuma, AG Richardson, L Srinivasan Journal of neurophysiology 114 (1), 746-760, 2015 | 5 | 2015 |
| Interpretable deep learning prediction of 3d assessment of cardiac function G Duffy, I Jain, B He, D Ouyang Pacific Symposium on BiocomputIng 2022, 231-241, 2021 | 4 | 2021 |
