Mert R. Sabuncu, Ph.D.
Affiliations: | 2006 | Princeton University, Princeton, NJ |
Area:
Biological & Biomedical,Information Sciences & SystemsGoogle:
"Mert Sabuncu"Parents
Sign in to add mentor| Peter J. Ramadge | grad student | 2006 | Princeton | |
| (Entropy-based image registration.) | ||||
| Polina Golland | post-doc | Cornell (Neurotree) | ||
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Publications
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| Gu Z, Jamison K, Sabuncu MR, et al. (2023) Human brain responses are modulated when exposed to optimized natural images or synthetically generated images. Communications Biology. 6: 1076 |
| Wang AQ, Yu EM, Dalca AV, et al. (2023) A robust and interpretable deep learning framework for multi-modal registration via keypoints. Medical Image Analysis. 90: 102962 |
| Gu Z, Jamison K, Sabuncu MR, et al. (2023) Modulating human brain responses via optimal natural image selection and synthetic image generation. Arxiv |
| Gu Z, Jamison K, Sabuncu M, et al. (2022) Personalized visual encoding model construction with small data. Communications Biology. 5: 1382 |
| Gu Z, Jamison KW, Khosla M, et al. (2021) NeuroGen: Activation optimized image synthesis for discovery neuroscience. Neuroimage. 247: 118812 |
| Zhang J, Liu Z, Zhang S, et al. (2020) Fidelity imposed network edit (FINE) for solving ill-posed image reconstruction. Neuroimage. 116579 |
| Yeo BT, Sabuncu M, Mohlberg H, et al. (2020) What Data to Co-register for Computing Atlases. Proceedings. Ieee International Conference On Computer Vision. 2007 |
| Dalca AV, Yu E, Golland P, et al. (2019) Unsupervised Deep Learning for Bayesian Brain MRI Segmentation. Medical Image Computing and Computer-Assisted Intervention : Miccai ... International Conference On Medical Image Computing and Computer-Assisted Intervention. 11766: 356-365 |
| De Man Q, Haneda E, Claus B, et al. (2019) A two-dimensional feasibility study of deep learning-based feature detection and characterization directly from CT sinograms. Medical Physics. 46: e790-e800 |
| He T, Kong R, Holmes AJ, et al. (2019) Deep neural networks and kernel regression achieve comparable accuracies for functional connectivity prediction of behavior and demographics. Neuroimage. 116276 |