Hyunkwang Lee
Hyunkwang Lee
DeepHealth AI at RadNet
Verified email at radnet.com
Cited by
Cited by
Minerva: Enabling low-power, highly-accurate deep neural network accelerators
B Reagen, P Whatmough, R Adolf, S Rama, H Lee, SK Lee, ...
2016 ACM/IEEE 43rd Annual International Symposium on Computer Architecture …, 2016
Fully automated deep learning system for bone age assessment
H Lee, S Tajmir, J Lee, M Zissen, BA Yeshiwas, TK Alkasab, G Choy, ...
Journal of digital imaging 30 (4), 427-441, 2017
An explainable deep-learning algorithm for the detection of acute intracranial haemorrhage from small datasets
H Lee, S Yune, M Mansouri, M Kim, SH Tajmir, CE Guerrier, SA Ebert, ...
Nature Biomedical Engineering 3 (3), 173-182, 2019
14.3 A 28nm SoC with a 1.2 GHz 568nJ/prediction sparse deep-neural-network engine with> 0.1 timing error rate tolerance for IoT applications
PN Whatmough, SK Lee, H Lee, S Rama, D Brooks, GY Wei
2017 IEEE International Solid-State Circuits Conference (ISSCC), 242-243, 2017
Pixel-level deep segmentation: artificial intelligence quantifies muscle on computed tomography for body morphometric analysis
H Lee, FM Troschel, S Tajmir, G Fuchs, J Mario, FJ Fintelmann, S Do
Journal of digital imaging 30 (4), 487-498, 2017
Artificial intelligence-assisted interpretation of bone age radiographs improves accuracy and decreases variability
SH Tajmir, H Lee, R Shailam, HI Gale, JC Nguyen, SJ Westra, R Lim, ...
Skeletal radiology 48 (2), 275-283, 2019
A deep-learning system for fully-automated peripherally inserted central catheter (PICC) tip detection
H Lee, M Mansouri, S Tajmir, MH Lev, S Do
Journal of digital imaging 31 (4), 393-402, 2018
Machine friendly machine learning: interpretation of computed tomography without image reconstruction
H Lee, C Huang, S Yune, SH Tajmir, M Kim, S Do
Scientific reports 9 (1), 1-9, 2019
A multi-chip system optimized for insect-scale flapping-wing robots
X Zhang, M Lok, T Tong, S Chaput, SK Lee, B Reagen, H Lee, D Brooks, ...
2015 Symposium on VLSI Circuits (VLSI Circuits), C152-C153, 2015
Towards generative adversarial networks as a new paradigm for radiology education
SG Finlayson, H Lee, IS Kohane, L Oakden-Rayner
arXiv preprint arXiv:1812.01547, 2018
Urinary stone detection on CT images using deep convolutional neural networks: evaluation of model performance and generalization
A Parakh, H Lee, JH Lee, BH Eisner, DV Sahani, S Do
Radiology: Artificial Intelligence 1 (4), e180066, 2019
Practical window setting optimization for medical image deep learning
H Lee, M Kim, S Do
arXiv preprint arXiv:1812.00572, 2018
Beyond human perception: sexual dimorphism in hand and wrist radiographs is discernible by a deep learning model
S Yune, H Lee, M Kim, SH Tajmir, MS Gee, S Do
Journal of digital imaging 32 (4), 665-671, 2019
Machine learning powered automatic organ classification for patient specific organ dose estimation
J Cho, E Lee, H Lee, B Liu, X Li, S Tajmir, D Sahani, S Do
Proceedings of the Society for Imaging Informatics in Medicine Annual Meeting, 2017
Patient risk stratification based on body composition derived from computed tomography images using machine learning
S Do, F Fintelmann, H Lee
US Patent App. 16/644,890, 2020
Systems, methods and media for automatically generating a bone age assessment from a radiograph
S Do, H Lee, M Gee, S Tajmir, T Alkasab
US Patent 10,991,093, 2021
Generalizable and Explainable Deep Learning in Medical Imaging with Small Data
H Lee
Harvard University, 2020
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