Bryan He
Bryan He
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Cited by
Cited by
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
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
Deep learning interpretation of echocardiograms
A Ghorbani, D Ouyang, A Abid, B He, JH Chen, RA Harrington, DH Liang, ...
npg Digital Medicine 3, 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
Learning the structure of generative models without labeled data
SH Bach, B He, A Ratner, C Ré
International Conference on Machine Learning, 2017
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, 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
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
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
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
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
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
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
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
Dynamic analysis of naive adaptive brain-machine interfaces
KC Kowalski, BD He, L Srinivasan
Neural Computation 25 (9), 2373-2420, 2013
A simple optimal binary representation of mosaic floorplans and Baxter permutations
Theoretical Computer Science 532, 40-50, 2014
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
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
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
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), 2022
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