| Fast and accurate estimation of quality of results in high-level synthesis with machine learning S Dai, Y Zhou, H Zhang, E Ustun, EFY Young, Z Zhang FCCM, 2018 | 129 | 2018 |
| Enabling online learning in lithography hotspot detection with information-theoretic feature optimization H Zhang, B Yu, EFY Young ICCAD, 2016 | 100 | 2016 |
| RippleFPGA: A routability-driven placement for large-scale heterogeneous FPGAs CW Pui, G Chen, WK Chow, KC Lam, J Kuang, P Tu, H Zhang, ... ICCAD, 2016 | 67 | 2016 |
| Fidelity imposed network edit (FINE) for solving ill-posed image reconstruction J Zhang, Z Liu, S Zhang, H Zhang, P Spincemaille, TD Nguyen, ... Neuroimage, 2020 | 65 | 2020 |
| Neural-ILT: Migrating ILT to neural networks for mask printability and complexity co-optimization B Jiang, L Liu, Y Ma, H Zhang, B Yu, EFY Young ICCAD, 2020 | 41 | 2020 |
| Extending LOUPE for K-space Under-sampling Pattern Optimization in Multi-coil MRI J Zhang, H Zhang, A Wang, Q Zhang, M Sabuncu, P Spincemaille, ... MICCAI-MLMIR, 2020 | 37 | 2020 |
| Rsanet: Recurrent slice-wise attention network for multiple sclerosis lesion segmentation H Zhang, J Zhang, Q Zhang, J Kim, S Zhang, SA Gauthier, P Spincemaille, ... MICCAI, 2019 | 35* | 2019 |
| QSMRim-Net: Imbalance-aware learning for identification of chronic active multiple sclerosis lesions on quantitative susceptibility maps H Zhang, TD Nguyen, J Zhang, M Marcille, P Spincemaille, Y Wang, ... NeuroImage: Clinical, 2022 | 34 | 2022 |
| A fast machine learning-based mask printability predictor for OPC acceleration B Jiang, H Zhang, J Yang, EFY Young ASPDAC, 2019 | 34 | 2019 |
| ALL-Net: Anatomical information lesion-wise loss function integrated into neural network for multiple sclerosis lesion segmentation H Zhang, J Zhang, C Li, EM Sweeney, P Spincemaille, TD Nguyen, ... NeuroImage: Clinical, 2021 | 33 | 2021 |
| Bilinear lithography hotspot detection H Zhang, F Zhu, H Li, EFY Young, B Yu ISPD, 2017 | 28 | 2017 |
| Spatially Covariant Lesion Segmentation H Zhang, R Wang, J Zhang, D Liu, C Li, J Li IJCAI, 2023 | 23* | 2023 |
| Geometric loss for deep multiple sclerosis lesion segmentation H Zhang, J Zhang, R Wang, Q Zhang, SA Gauthier, P Spincemaille, ... ISBI, 2021 | 23 | 2021 |
| Efficient folded attention for 3d medical image reconstruction and segmentation H Zhang, J Zhang, R Wang, Q Zhang, P Spincemaille, TD Nguyen, ... AAAI, 2021 | 21 | 2021 |
| QQ‐NET–using deep learning to solve quantitative susceptibility mapping and quantitative blood oxygen level dependent magnitude (QSM+ qBOLD or QQ) based oxygen extraction … J Cho, J Zhang, P Spincemaille, H Zhang, S Hubertus, Y Wen, R Jafari, ... Magnetic Resonance in Medicine (MRM), 2022 | 20 | 2022 |
| Ensembling low precision models for binary biomedical image segmentation T Ma, H Zhang, H Ong, A Vora, TD Nguyen, A Gupta, Y Wang, ... WACV, 2021 | 18 | 2021 |
| Minimizing thermal gradient and pumping power in 3D IC liquid cooling network design G Chen, J Kuang, Z Zeng, H Zhang, EFY Young, B Yu DAC, 2017 | 17 | 2017 |
| LARO: Learned acquisition and reconstruction optimization to accelerate quantitative susceptibility mapping J Zhang, P Spincemaille, H Zhang, TD Nguyen, C Li, J Li, I Kovanlikaya, ... NeuroImage, 2023 | 11 | 2023 |
| Brain oxygen extraction and neural tissue susceptibility are associated with cognitive impairment in older individuals GC Chiang, J Cho, J Dyke, H Zhang, Q Zhang, M Tokov, T Nguyen, ... Journal of Neuroimaging 32 (4), 697-709, 2022 | 11 | 2022 |
| Bayesian learning of probabilistic dipole inversion for quantitative susceptibility mapping J Zhang, H Zhang, M Sabuncu, P Spincemaille, T Nguyen, Y Wang MIDL, 2020 | 10 | 2020 |
Jinwei ZhangJohns Hopkins University, Cornell UniversityVerified email at jhu.edu
