Sabuncu, Mert R.

The organization of the human cerebral cortex estimated by intrinsic functional connectivity

Thomas Yeo, B. T., Krienen, F. M., Sepulcre, J., Sabuncu, M. R., Lashkari, D., Hollinshead, M., Roffman, J. L., Smoller, J. W., Zöllei, L., Polimeni, J. R., et al. (2011) · Journal of Neurophysiology

Functional Brain Connectivity StudiesAdvanced Neuroimaging Techniques and Applications

Cited by 9,182

The influence of head motion on intrinsic functional connectivity MRI

Van Dijk, K. R. A., Sabuncu, M. R., & Buckner, R. L. (2012) · NeuroImage

Functional Brain Connectivity StudiesAdvanced MRI Techniques and Applications

Cited by 2,344

Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels

Zhang, Z., & Sabuncu, M. R. (2018) · arXiv

Machine Learning and Data ClassificationAdvanced Neural Network Applications

Cited by 1,907

Multi-atlas segmentation of biomedical images: A survey

Iglesias, J. E., & Sabuncu, M. R. (2015) · Medical Image Analysis

Medical Image Segmentation TechniquesMedical Imaging and Analysis

Cited by 667

An Unsupervised Learning Model for Deformable Medical Image Registration

Balakrishnan, G., Zhao, A., Sabuncu, M. R., Dalca, A. V., & Guttag, J. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

Medical Image Segmentation TechniquesDomain Adaptation and Few-Shot Learning

Cited by 599

Unsupervised Learning of Probabilistic Diffeomorphic Registration for Images and Surfaces

Dalca, A. V., Balakrishnan, G., Guttag, J., & Sabuncu, M. R. (2019) · Medical Image Analysis

Medical Image Segmentation Techniques3D Shape Modeling and Analysis

Cited by 467

Global signal regression strengthens association between resting-state functional connectivity and behavior

Li, J., Kong, R., Liégeois, R., Orban, C., Tan, Y., Sun, N., Holmes, A. J., Sabuncu, M. R., Ge, T., & Yeo, B. T. T. (2019) · NeuroImage

Functional Brain Connectivity StudiesAdvanced MRI Techniques and Applications

Cited by 380

Statistical analysis of longitudinal neuroimage data with Linear Mixed Effects models

Bernal-Rusiel, J. L., Greve, D. N., Reuter, M., Fischl, B., & Sabuncu, M. R. (2013) · NeuroImage

Statistical Methods and Bayesian InferenceDementia and Cognitive Impairment Research

Cited by 360

Stepwise Connectivity of the Modal Cortex Reveals the Multimodal Organization of the Human Brain

Sepulcre, J., Sabuncu, M. R., Yeo, T. B., Liu, H., & Johnson, K. A. (2012) · Journal of Neuroscience

Functional Brain Connectivity StudiesMultisensory perception and integration

Cited by 323

Deep neural networks and kernel regression achieve comparable accuracies for functional connectivity prediction of behavior and demographics

He, T., Kong, R., Holmes, A. J., Nguyen, M., Sabuncu, M. R., Eickhoff, S. B., Bzdok, D., Feng, J., & Yeo, B. T. T. (2020) · NeuroImage

Functional Brain Connectivity StudiesAdvanced Neuroimaging Techniques and Applications

Cited by 271

UniverSeg: Universal Medical Image Segmentation

Butoi, V. I., Gonzalez Ortiz, J. J., Ma, T., Sabuncu, M. R., Guttag, J., & Dalca, A. V. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV)

Medical Image Segmentation TechniquesDomain Adaptation and Few-Shot Learning

Cited by 195

Clinical prediction from structural brain MRI scans: A large-scale empirical study

Initiative, F. T. A. D. N., Sabuncu, M. R., & Konukoglu, E. (2015) · Neuroinformatics

Functional Brain Connectivity StudiesDementia and Cognitive Impairment Research

Cited by 161

Spatiotemporal linear mixed effects modeling for the mass-univariate analysis of longitudinal neuroimage data

Bernal-Rusiel, J. L., Reuter, M., Greve, D. N., Fischl, B., & Sabuncu, M. R. (2013) · NeuroImage

Functional Brain Connectivity StudiesStatistical Methods and Bayesian Inference

Cited by 125

In vivo characterization of the early states of the amyloid-beta network

Sepulcre, J., Sabuncu, M. R., Becker, A., Sperling, R., & Johnson, K. A. (2013) · Brain

Alzheimer's disease research and treatmentsDementia and Cognitive Impairment Research

Cited by 117

Hierarchical Organization of Tau and Amyloid Deposits in the Cerebral Cortex

Sepulcre, J., Grothe, M. J., Sabuncu, M., Chhatwal, J., Schultz, A. P., Hanseeuw, B., El Fakhri, G., Sperling, R., & Johnson, K. A. (2017) · JAMA Neurology

Dementia and Cognitive Impairment ResearchAlzheimer's disease research and treatments

Cited by 73

Rethinking clinical trials for medical AI with dynamic deployments of adaptive systems

Rosenthal, J. T., Beecy, A., & Sabuncu, M. R. (2025) · npj Digital Medicine

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 59

Learning across Diverse Biomedical Data Modalities and Cohorts: Challenges and Opportunities for Innovation

Rajendran, S., Pan, W., Sabuncu, M. R., Chen, Y., Zhou, J., & Wang, F. (2024) · Patterns

Machine Learning in HealthcareArtificial Intelligence in Healthcare and Education

Cited by 47

Deep Neural Networks and Kernel Regression Achieve Comparable Accuracies for Functional Connectivity Prediction of Behavior and Demographics

He, T., Kong, R., Holmes, A. J., Nguyen, M., Sabuncu, M. R., Eickhoff, S. B., Bzdok, D., Feng, J., & Yeo, B. T. T. (2018) · bioRxiv (Cold Spring Harbor Laboratory)

Functional Brain Connectivity StudiesAdvanced Neuroimaging Techniques and Applications

Cited by 16

A Deep‐Learning Model for Multi‐class Audio Classification of Vocal Fold Pathologies in Office Stroboscopy

Kim, Y. E., Dobko, M., Li, H., Shao, T., Periyakoil, P., Tipton, C., Colasacco, C., Serpedin, A., Elemento, O., Sabuncu, M., et al. (2025) · The Laryngoscope

Voice and Speech DisordersPhonocardiography and Auscultation Techniques

Cited by 9

Intelligence plays dice: Stochasticity is essential for machine learning

Sabuncu, M. R. (2020) · arXiv

Neural Networks and ApplicationsStochastic Gradient Optimization Techniques

Cited by 3