Kayhan Batmanghelich
Kayhan Batmanghelich
Assistant Professor, Boston University
Потвърден имейл адрес: pitt.edu - Начална страница
Deep ordinal regression network for monocular depth estimation
H Fu, M Gong, C Wang, K Batmanghelich, D Tao
Proceedings of the IEEE conference on computer vision and pattern …, 2018
Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge
S Bakas, M Reyes, A Jakab, S Bauer, M Rempfler, A Crimi, RT Shinohara, ...
arXiv preprint arXiv:1811.02629, 2018
Prediction of MCI to AD conversion, via MRI, CSF biomarkers, and pattern classification
C Davatzikos, P Bhatt, LM Shaw, KN Batmanghelich, JQ Trojanowski
Neurobiology of aging 32 (12), 2322. e19-2322. e27, 2011
Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline
Y Fan, N Batmanghelich, CM Clark, C Davatzikos, ...
Neuroimage 39 (4), 1731-1743, 2008
Geometry-consistent generative adversarial networks for one-sided unsupervised domain mapping
H Fu, M Gong, C Wang, K Batmanghelich, K Zhang, D Tao
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019
Nonparametric Spherical Topic Modeling with Word Embeddings
S Batmanghelich, Kayhan and Saeedi, Ardavan and Narasimhan, Karthik and Gershman
arXiv preprint arXiv:1604.00126, 2016
Generative-discriminative basis learning for medical imaging
NK Batmanghelich, B Taskar, C Davatzikos
IEEE transactions on medical imaging 31 (1), 51-69, 2011
Explanation by Progressive Exaggeration
S Singla, B Pollack, J Chen, K Batmanghelich
International Conference on Learning Representations, 2019
Twin Auxiliary Classifiers GAN
M Gong, Y Xu, C Li, K Zhang, K Batmanghelich
NeurIPs preprint arXiv:1907.02690, 2019
An efficient and provable approach for mixture proportion estimation using linear independence assumption
X Yu, T Liu, M Gong, K Batmanghelich, D Tao
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018
Joint modeling of imaging and genetics
NK Batmanghelich, AV Dalca, MR Sabuncu, P Golland
Information Processing in Medical Imaging: 23rd International Conference …, 2013
Can contrastive learning avoid shortcut solutions?
J Robinson, L Sun, K Yu, K Batmanghelich, S Jegelka, S Sra
Advances in neural information processing systems 34, 4974-4986, 2021
Weakly supervised disentanglement by pairwise similarities
J Chen, K Batmanghelich
Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 3495-3502, 2020
Disease classification and prediction via semi-supervised dimensionality reduction
KN Batmanghelich, HY Dong, KM Pohl, B Taskar, C Davatzikos
2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro …, 2011
Probabilistic modeling of imaging, genetics and diagnosis
NK Batmanghelich, A Dalca, G Quon, M Sabuncu, P Golland
IEEE transactions on medical imaging 35 (7), 1765-1779, 2016
Causal discovery in the presence of measurement error: Identifiability conditions
K Zhang, M Gong, J Ramsey, K Batmanghelich, P Spirtes, C Glymour
arXiv preprint arXiv:1706.03768, 2017
Transfer learning with label noise
X Yu, T Liu, M Gong, K Zhang, K Batmanghelich, D Tao
arXiv preprint arXiv:1707.09724, 2017
A general and unifying framework for feature construction, in image-based pattern classification
N Batmanghelich, B Taskar, C Davatzikos
Information Processing in Medical Imaging: 21st International Conference …, 2009
Unsupervised discovery of emphysema subtypes in a large clinical cohort
P Binder, NK Batmanghelich, RSJ Estepar, P Golland
Machine Learning in Medical Imaging: 7th International Workshop, MLMI 2016 …, 2016
Deep diffeomorphic normalizing flows
H Salman, P Yadollahpour, T Fletcher, K Batmanghelich
arXiv preprint arXiv:1810.03256, 2018
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