| Using individualized brain network for analyzing structural covariance of the cerebral cortex in Alzheimer's patients HJ Kim, JH Shin, CE Han, HJ Kim, DL Na, SW Seo, JK Seong, ... Frontiers in neuroscience 10, 394, 2016 | 42 | 2016 |
| The role of AI in prostate MRI quality and interpretation: Opportunities and challenges H Kim, SW Kang, JH Kim, H Nagar, M Sabuncu, DJA Margolis, CK Kim European journal of radiology 165, 110887, 2023 | 19 | 2023 |
| Equivariant spherical deconvolution: Learning sparse orientation distribution functions from spherical data A Elaldi, N Dey, H Kim, G Gerig International Conference on Information Processing in Medical Imaging, 267-278, 2021 | 18 | 2021 |
| Q-space Conditioned Translation Networks for Directional Synthesis of Diffusion Weighted Images from Multi-modal Structural MRI M Ren, H Kim, N Dey, G Gerig Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th …, 2021 | 15 | 2021 |
| Tract-specific correlates of neuropsychological deficits in patients with subcortical vascular cognitive impairment NY Jung, CE Han, HJ Kim, SW Yoo, HJ Kim, EJ Kim, DL Na, SN Lockhart, ... Journal of Alzheimer’s Disease 50 (4), 1125-1135, 2016 | 15 | 2016 |
| Learning to compare longitudinal images H Kim, MR Sabuncu arXiv preprint arXiv:2304.02531, 2023 | 12 | 2023 |
| Microparticle-based biochemical sensing using optical coherence tomography and deep learning S Shah, CN Yu, M Zheng, H Kim, MS Eggleston ACS nano 15 (6), 9764-9774, 2021 | 12 | 2021 |
| A framework for interpretability in machine learning for medical imaging AQ Wang, BK Karaman, H Kim, J Rosenthal, R Saluja, SI Young, ... IEEE Access, 2024 | 7 | 2024 |
| Pulse sequence dependence of a simple and interpretable deep learning method for detection of clinically significant prostate cancer using multiparametric MRI H Kim, DJA Margolis, H Nagar, MR Sabuncu Academic Radiology 30 (5), 966-970, 2023 | 5 | 2023 |
| Empirical analysis of a segmentation foundation model in prostate imaging H Kim, VI Butoi, AV Dalca, MR Sabuncu International Conference on Medical Image Computing and Computer-Assisted …, 2023 | 4 | 2023 |
| A framework to construct a longitudinal dw-mri infant atlas based on mixed effects modeling of dodf coefficients H Kim, M Styner, J Piven, G Gerig Computational Diffusion MRI: MICCAI Workshop, Shenzhen, China, October 2019 …, 2020 | 4 | 2020 |
| Hierarchical geodesic modeling on the diffusion orientation distribution function for longitudinal DW-MRI analysis H Kim, S Hong, M Styner, J Piven, K Botteron, G Gerig Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd …, 2020 | 2 | 2020 |
| Longitudinal structural connectivity in the developing brain with projective non-negative matrix factorization H Kim, J Piven, G Gerig Medical Imaging 2019: Image Processing 10949, 189-196, 2019 | 2 | 2019 |
| Adapting to Shifting Correlations with Unlabeled Data Calibration M Nguyen, AQ Wang, H Kim, MR Sabuncu European Conference on Computer Vision, 230-246, 2024 | 1 | 2024 |
| Effective Segmentation of Post-Treatment Gliomas Using Simple Approaches: Artificial Sequence Generation and Ensemble Models H Kim, L Milecki, MC Moghadam, F Liu, M Nguyen, E Qiu, A Thanki, ... arXiv preprint arXiv:2409.08143, 2024 | 1 | 2024 |
| Knockout: A simple way to handle missing inputs M Nguyen, BK Karaman, H Kim, AQ Wang, F Liu, MR Sabuncu arXiv preprint arXiv:2405.20448, 2024 | 1 | 2024 |
| Automated Monitoring for Optical Coherence Tomography-based Biosensing Using Deep Learning H Kim, CN Yu, W Kennedy, M Eggleston, S Shah 2020 IEEE Photonics Conference (IPC), 1-2, 2020 | 1 | 2020 |
| Learning-based inference of longitudinal image changes: Applications in embryo development, wound healing, and aging brain H Kim, BK Karaman, Q Zhao, AQ Wang, MR Sabuncu, ... Proceedings of the National Academy of Sciences 122 (8), e2411492122, 2025 | | 2025 |
| Robust Learning via Conditional Prevalence Adjustment M Nguyen, AQ Wang, H Kim, MR Sabuncu Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2024 | | 2024 |
| Recent Advances, Applications and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2022 Symposium S Hegselmann, H Zhou, Y Zhou, J Chien, S Nagaraj, N Hulkund, S Bhave, ... | | |
Mert R. SabuncuCornell University, Cornell Tech, Weill Cornell MedicineOverená e-mailová adresa na: cornell.edu
