| Deep 3D convolutional encoder networks with shortcuts for multiscale feature integration applied to multiple sclerosis lesion segmentation T Brosch, LYW Tang, Y Yoo, DKB Li, A Traboulsee, R Tam IEEE transactions on medical imaging 35 (5), 1229-1239, 2016 | 697 | 2016 |
| Automated quantification of CT patterns associated with COVID-19 from chest CT S Chaganti, P Grenier, A Balachandran, G Chabin, S Cohen, T Flohr, ... Radiology: Artificial Intelligence 2 (4), e200048, 2020 | 176 | 2020 |
| Deep learning of joint myelin and T1w MRI features in normal-appearing brain tissue to distinguish between multiple sclerosis patients and healthy controls Y Yoo, LYW Tang, T Brosch, DKB Li, S Kolind, I Vavasour, A Rauscher, ... NeuroImage: Clinical 17, 169-178, 2018 | 112 | 2018 |
| Contrastive self-supervised learning from 100 million medical images with optional supervision FC Ghesu, B Georgescu, A Mansoor, Y Yoo, D Neumann, P Patel, ... Journal of Medical Imaging 9 (6), 064503-064503, 2022 | 84 | 2022 |
| Deep learning of image features from unlabeled data for multiple sclerosis lesion segmentation Y Yoo, T Brosch, A Traboulsee, DKB Li, R Tam Machine Learning in Medical Imaging: 5th International Workshop, MLMI 2014 …, 2014 | 78 | 2014 |
| Deep learning of brain lesion patterns and user-defined clinical and MRI features for predicting conversion to multiple sclerosis from clinically isolated syndrome Y Yoo, LYW Tang, DKB Li, L Metz, S Kolind, AL Traboulsee, RC Tam Computer Methods in Biomechanics and Biomedical Engineering: Imaging …, 2019 | 74 | 2019 |
| Modeling the variability in brain morphology and lesion distribution in multiple sclerosis by deep learning T Brosch, Y Yoo, DKB Li, A Traboulsee, R Tam Medical Image Computing and Computer-Assisted Intervention–MICCAI 2014: 17th …, 2014 | 74 | 2014 |
| Imaging outcome measures of neuroprotection and repair in MS: a consensus statement from NAIMS J Oh, D Ontaneda, C Azevedo, EC Klawiter, M Absinta, DL Arnold, ... Neurology 92 (11), 519-533, 2019 | 72 | 2019 |
| Quantifying and leveraging predictive uncertainty for medical image assessment FC Ghesu, B Georgescu, A Mansoor, Y Yoo, E Gibson, RS Vishwanath, ... Medical Image Analysis 68, 101855, 2021 | 68 | 2021 |
| Deep learning of brain lesion patterns for predicting future disease activity in patients with early symptoms of multiple sclerosis Y Yoo, LW Tang, T Brosch, DKB Li, L Metz, A Traboulsee, R Tam Deep Learning and Data Labeling for Medical Applications: First …, 2016 | 60 | 2016 |
| Rapid myelin water imaging in human cervical spinal cord E Ljungberg, I Vavasour, R Tam, Y Yoo, A Rauscher, DKB Li, ... Magnetic resonance in medicine 78 (4), 1482-1487, 2017 | 46 | 2017 |
| Machine learning automatically detects COVID-19 using chest CTs in a large multicenter cohort EJ Mortani Barbosa Jr, B Georgescu, S Chaganti, GB Aleman, JB Cabrero, ... European radiology 31 (11), 8775-8785, 2021 | 34 | 2021 |
| Non-Local Spatial Regularization of MRI T2 Relaxation Images for Myelin Water Quantification Y Yoo, R Tam International Conference on Medical Image Computing and Computer-Assisted …, 2013 | 32 | 2013 |
| Apparatus and method for obtaining motion adaptive high dynamic range image H Oh, WH Choe, S Lee, H Song, SC Park, YJ Yoo, JH Kwon, K Lee US Patent App. 13/485,357, 2012 | 27 | 2012 |
| Medical Image Computing and Computer-Assisted Intervention (MICCAI 2013) T Brosch, Y Yoo, DKB Li, A Traboulsee, R Tam, P Golland, N Hata, ... Springer, 2014 | 22 | 2014 |
| Automated detection of critical findings in multi-parametric brain MRI using a system of 3D neural networks K Nael, E Gibson, C Yang, P Ceccaldi, Y Yoo, J Das, A Doshi, ... Scientific reports 11 (1), 6876, 2021 | 21 | 2021 |
| Brain myelin water fraction and diffusion tensor imaging atlases for 9‐10 year‐old children SR Morris, RD Holmes, AV Dvorak, H Liu, Y Yoo, IM Vavasour, S Mazabel, ... Journal of Neuroimaging 30 (2), 150-160, 2020 | 20 | 2020 |
| CMOS image sensor noise reduction method for image signal processor in digital cameras and camera phones Y Yoo, SD Lee, W Choe, CY Kim Digital Photography III 6502, 263-272, 2007 | 19 | 2007 |
| 3d tomographic pattern synthesis for enhancing the quantification of covid-19 S Liu, B Georgescu, Z Xu, Y Yoo, G Chabin, S Chaganti, S Grbic, S Piat, ... arXiv preprint arXiv:2005.01903, 2020 | 18 | 2020 |
| Quantification of tomographic patterns associated with COVID-19 from chest CT S Chaganti, A Balachandran, G Chabin, S Cohen, T Flohr, B Georgescu, ... arXiv preprint arXiv:2004.01279, 2020 | 18 | 2020 |
