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Multi-Atlas Skull-Stripping

Academic radiology, 2013-12, Vol.20 (12), p.1566-1576

2013

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Natural history of the aging spine: a cross-sectional analysis of spinopelvic parameters in the asymptomatic population

Journal of neurosurgery. Spine, 2020-01, Vol.32 (1), p.63-68

2020

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Control-group feature normalization for multivariate pattern analysis of structural MRI data using the support vector machine

NeuroImage (Orlando, Fla.), 2016-05, Vol.132, p.157-166

2016

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Analytic estimation of statistical significance maps for support vector machine based multi-variate image analysis and classification

NeuroImage (Orlando, Fla.), 2013-09, Vol.78, p.270-283

2013

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Breast DCE-MRI Kinetic Heterogeneity Tumor Markers: Preliminary Associations With Neoadjuvant Chemotherapy Response

Translational oncology, 2015-06, Vol.8 (3), p.154-162

2015

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Exploring intentions of physician-scientist trainees: factors influencing MD and MD/PhD interest in research careers

BMC medical education, 2017-07, Vol.17 (1), p.115-115, Article 115

2017

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Automated Tumor Volumetry Using Computer-Aided Image Segmentation

Academic radiology, 2015-05, Vol.22 (5), p.653-661

2015

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Multi-Parameter Ensemble Learning for Automated Vertebral Body Segmentation in Heterogeneously Acquired Clinical MR Images

IEEE journal of translational engineering in health and medicine, 2017-01, Vol.5, p.1-12

2017

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Isolated Transverse Process Fractures: A Systematic Analysis

World neurosurgery, 2017-04, Vol.100, p.336-341

2017

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Diagnostic potential of structural neuroimaging for depression from a multi-ethnic community sample

BJPsych open, 2016-07, Vol.2 (4), p.247-254

2016

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CHIMERA: Clustering of Heterogeneous Disease Effects via Distribution Matching of Imaging Patterns

IEEE transactions on medical imaging, 2016-02, Vol.35 (2), p.612-621

2016

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Imaging Biomarker Development for Lower Back Pain Using Machine Learning: How Image Analysis Can Help Back Pain

Methods in molecular biology (Clifton, N.J.), 2022, Vol.2393, p.623

2022

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Interpreting support vector machine models for multivariate group wise analysis in neuroimaging

Medical image analysis, 2015-08, Vol.24 (1), p.190-204

2015

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Predicting Spinal Surgery Candidacy From Imaging Data Using Machine Learning

Neurosurgery, 2021-07, Vol.89 (1), p.116-121

2021

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Relationship Between Superior Semicircular Canal Dehiscence Volume with Clinical Symptoms: Case Series

World neurosurgery, 2021-12, Vol.156, p.e345-e350

2021

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Eigenrank by committee: Von-Neumann entropy based data subset selection and failure prediction for deep learning based medical image segmentation

Medical image analysis, 2021-01, Vol.67, p.101834-101834, Article 101834

2021

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Lumbar Spinal Canal Segmentation in Cases with Lumbar Stenosis Using Deep-U-Net Ensembles

World neurosurgery, 2023-10, Vol.178, p.e135-e140

2023

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Multivariate Neural Connectivity Patterns in Early Infancy Predict Later Autism Symptoms

Biological psychiatry : cognitive neuroscience and neuroimaging, 2021-01, Vol.6 (1), p.59-69

2021

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A deep learning approach to spine segmentation using a feed-forward chain of pixel-wise convolutional networks

2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), 2018, p.868-871

2018

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Structural Changes in the Cervicomedullary Junction in Adult Chiari Patients

World neurosurgery, 2022-12, Vol.168, p.e621-e625

2022

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Machine learning predicts cerebral vasospasm in patients with subarachnoid haemorrhageResearch in context

EBioMedicine, 2024-07, Vol.105, p.105206

2024

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Deformable registration for quantifying longitudinal tumor changes during neoadjuvant chemotherapy

Magnetic resonance in medicine, 2015-06, Vol.73 (6), p.2343-2356

2015

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Novel Method of Measuring Canal Dehiscence and Evaluation of its Potential as a Predictor of Symptom Outcomes After Middle Fossa Craniotomy

Neurosurgery, 2018-09, Vol.83 (3), p.459-464

2018

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Addressing Confounding in Predictive Models with an Application to Neuroimaging

The international journal of biostatistics, 2016-05, Vol.12 (1), p.31-44

2016

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Machine learning predicts cerebral vasospasm in patients with subarachnoid haemorrhage

EBioMedicine, 2024-07, Vol.105, p.105206, Article 105206

2024

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Multi-Atlas Skull-Stripping

Natural history of the aging spine: a cross-sectional analysis of spinopelvic parameters in the asymptomatic population

Control-group feature normalization for multivariate pattern analysis of structural MRI data using the support vector machine

Analytic estimation of statistical significance maps for support vector machine based multi-variate image analysis and classification

Breast DCE-MRI Kinetic Heterogeneity Tumor Markers: Preliminary Associations With Neoadjuvant Chemotherapy Response

Exploring intentions of physician-scientist trainees: factors influencing MD and MD/PhD interest in research careers

Automated Tumor Volumetry Using Computer-Aided Image Segmentation

Multi-Parameter Ensemble Learning for Automated Vertebral Body Segmentation in Heterogeneously Acquired Clinical MR Images

Isolated Transverse Process Fractures: A Systematic Analysis

Diagnostic potential of structural neuroimaging for depression from a multi-ethnic community sample

CHIMERA: Clustering of Heterogeneous Disease Effects via Distribution Matching of Imaging Patterns

Imaging Biomarker Development for Lower Back Pain Using Machine Learning: How Image Analysis Can Help Back Pain

Interpreting support vector machine models for multivariate group wise analysis in neuroimaging

Predicting Spinal Surgery Candidacy From Imaging Data Using Machine Learning

Relationship Between Superior Semicircular Canal Dehiscence Volume with Clinical Symptoms: Case Series

Eigenrank by committee: Von-Neumann entropy based data subset selection and failure prediction for deep learning based medical image segmentation

Lumbar Spinal Canal Segmentation in Cases with Lumbar Stenosis Using Deep-U-Net Ensembles

Multivariate Neural Connectivity Patterns in Early Infancy Predict Later Autism Symptoms

A deep learning approach to spine segmentation using a feed-forward chain of pixel-wise convolutional networks

Structural Changes in the Cervicomedullary Junction in Adult Chiari Patients

Machine learning predicts cerebral vasospasm in patients with subarachnoid haemorrhageResearch in context

Deformable registration for quantifying longitudinal tumor changes during neoadjuvant chemotherapy

Novel Method of Measuring Canal Dehiscence and Evaluation of its Potential as a Predictor of Symptom Outcomes After Middle Fossa Craniotomy

Addressing Confounding in Predictive Models with an Application to Neuroimaging

Machine learning predicts cerebral vasospasm in patients with subarachnoid haemorrhage

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