Joseph A. Maldjian

发表

Ramon Casanova, Ryali Srikanth, Aaron H. Baer, 2006 .

A Novel Hierarchical Level Set with AR-boost for White Matter Lesion Segmentation in Diabetes

Sriraam Natarajan, Joseph A. Maldjian, Baidya Nath Saha, 2012, 2012 11th International Conference on Machine Learning and Applications.

Multidimensional and Multiresolution Ensemble Networks for Brain Tumor Segmentation

Gowtham Krishnan Murugesan, Joseph A. Maldjian, Baowei Fei, 2019, BrainLes@MICCAI.

Expert Systems in Radiologic Diagnosis: An Application Program.

Joseph A. Maldjian, G Hermann, 1990 .

A Fully Automated Deep Learning Network for Brain Tumor Segmentation

Joseph A. Maldjian, Baowei Fei, Benjamin C. Wagner, 2020, Tomography.

Erratum to: Multi-Atlas Library for Eliminating Normalization Failures in Non-Human Primates

Joseph A. Maldjian, Christopher T. Whitlow, Carol A. Shively, 2016, Neuroinformatics.

Determining the Optimal Number of MEG Trials: A Machine Learning and Speech Decoding Perspective

Albert Montillo, Joseph A. Maldjian, Debadatta Dash, 2018, BI.

Image segmentation and registration algorithm to collect thoracic skeleton semilandmarks for characterization of age and sex-based thoracic morphology variation

Joseph A. Maldjian, Joel D. Stitzel, Callistus M. Nguyen, 2015, Comput. Biol. Medicine.

Single season changes in resting state network power and the connectivity between regions distinguish head impact exposure level in high school and youth football players

Gowtham Krishnan Murugesan, Albert Montillo, Joseph A. Maldjian, 2018, Medical Imaging.

Evaluating the Impact of Different Factors on Voxel-Based Classification Methods of ADNI Structural MRI Brain Images

Ramon Casanova, Joseph A. Maldjian, Mark A. Espeland, 2011 .

Vervet MRI Atlas and Label Map for Fully Automated Morphometric Analyses

Joseph A. Maldjian, Christopher T. Whitlow, David P. Friedman, 2014, Neuroinformatics.

Brain Tumor IDH, 1p/19q, and MGMT Molecular Classification Using MRI-based Deep Learning: Effect of Motion and Motion Correction

Joseph A. Maldjian, Baowei Fei, Benjamin C. Wagner, 2020, bioRxiv.

Brain Activation during Facial Emotion Processing

David C. Alsop, Joseph A. Maldjian, Raquel E. Gur, 2002, NeuroImage.

Quantifying the association between white matter integrity changes and subconcussive head impact exposure from a single season of youth and high school football using 3D convolutional neural networks

Albert Montillo, Joseph A. Maldjian, Christopher T. Whitlow, 2018, Medical Imaging.

High Dimensional Classification of Structural MRI Alzheimer’s Disease Data Based on Large Scale Regularization

Ramon Casanova, Joseph A. Maldjian, Christopher T. Whitlow, 2011, Front. Neuroinform..

Fully Automated Brain Tumor Segmentation and Survival Prediction of Gliomas using Deep Learning and MRI

Ben Wagner, Joseph A. Maldjian, Baowei Fei, 2019, bioRxiv.

Quantifying the Impact of Type 2 Diabetes on Brain Perfusion Using Deep Neural Networks

Albert Montillo, Joseph A. Maldjian, Jasmin Divers, 2017, DLMIA/ML-CDS@MICCAI.

Head Impact Exposure in Youth Football: Elementary School Ages 9–12 Years and the Effect of Practice Structure

Joseph A. Maldjian, Christopher T. Whitlow, Joel D. Stitzel, 2013, Annals of Biomedical Engineering.

Multidimensional and Multiresolution Ensemble Networks for Brain Tumor Segmentation

Chandan Ganesh, Sahil Nalawade, Ben Wagner, 2019, bioRxiv.

A Deep Learning Pipeline for Automatic Skull Stripping and Brain Segmentation

Joseph A. Maldjian, Chandan Ganesh Bangalore Yogananda, Benjamin C. Wagner, 2019, 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019).

AR-Boost: Reducing Overfitting by a Robust Data-Driven Regularization Strategy

Sriraam Natarajan, Joseph A. Maldjian, Nilanjan Ray, 2013, ECML/PKDD.

Fully Automated Brain Tumor Segmentation and Survival Prediction of Gliomas Using Deep Learning and MRI

Gowtham Krishnan Murugesan, Joseph A. Maldjian, Baowei Fei, 2019, BrainLes@MICCAI.

Multi-Atlas Library for Eliminating Normalization Failures in Non-Human Primates

Joseph A. Maldjian, Christopher T. Whitlow, Carol A. Shively, 2015, Neuroinformatics.

Resurrecting Brinley Plots for a Novel Use: Meta-Analyses of Functional Brain Imaging Data in Older Adults

Paul J. Laurienti, Ann M. Peiffer, Joseph A. Maldjian, 2007, Int. J. Biomed. Imaging.

Relational learning helps in three-way classification of Alzheimer patients from structural magnetic resonance images of the brain

Kristian Kersting, Sriraam Natarajan, Joseph A. Maldjian, 2014, Int. J. Mach. Learn. Cybern..

Automatic 1D convolutional neural network-based detection of artifacts in MEG acquired without electrooculography or electrocardiography

Albert Montillo, Joseph A. Maldjian, Christopher T. Whitlow, 2017, 2017 International Workshop on Pattern Recognition in Neuroimaging (PRNI).

An adaptive staircase procedure for the E-Prime programming environment

W. David Hairston, Joseph A. Maldjian, W. Hairston, 2009, Comput. Methods Programs Biomed..

A Machine Learning Pipeline for Three-Way Classification of Alzheimer Patients from Structural Magnetic Resonance Images of the Brain

Kristian Kersting, Sriraam Natarajan, Joseph A. Maldjian, 2012, 2012 11th International Conference on Machine Learning and Applications.

Semi-automated landmark identification using label maps in an MNI atlas

Joseph A. Maldjian, Christopher T. Whitlow, Joel D. Stitzel, 2011 .

Fully Automated Processing of fMRI Data in SPM: from MRI Scanner to PACS

Aaron H. Baer, Paul J. Laurienti, Jonathan H. Burdette, 2009, Neuroinformatics.

Dietary Caffeine Consumption Modulates fMRI Measures

Paul J. Laurienti, Jonathan H. Burdette, Joseph A. Maldjian, 2002, NeuroImage.

The impact of temporal regularization on estimates of the BOLD hemodynamic response function: A comparative analysis

Paul J. Laurienti, Joseph A. Maldjian, John T. Serences, 2008, NeuroImage.

Changes in resting state MRI networks from a single season of football distinguishes controls, low, and high head impact exposure

Albert Montillo, Joseph A. Maldjian, Christopher T. Whitlow, 2017, 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017).

Using Convolutional Neural Networks to Automatically Detect Eye-Blink Artifacts in Magnetoencephalography Without Resorting to Electrooculography

Albert Montillo, Joseph A. Maldjian, Christopher T. Whitlow, 2017, MICCAI.

Estimation of false discovery rates for wavelet-denoised statistical parametric maps

Ramon Casanova, Paul J. Laurienti, Ann M. Peiffer, 2006, NeuroImage.

Graph theoretical analysis of resting-state MEG data: Identifying interhemispheric connectivity and the default mode

Joseph A. Maldjian, Christopher T. Whitlow, Elizabeth M. Davenport, 2014, NeuroImage.

An automated method for neuroanatomic and cytoarchitectonic atlas-based interrogation of fMRI data sets

Paul J. Laurienti, Jonathan H. Burdette, Joseph A. Maldjian, 2003, NeuroImage.

Biological parametric mapping: A statistical toolbox for multimodality brain image analysis

Ramon Casanova, Ryali Srikanth, Paul J. Laurienti, 2007, NeuroImage.

BrainNET: Inference of brain network topology using Machine Learning

Chandan Ganesh, Sahil Nalawade, Elizabeth M Davenport, 2019, Brain Connect..

Multi-TI Arterial Spin Labeling MRI with Variable TR and Bolus Duration for Cerebral Blood Flow and Arterial Transit Time Mapping

Joseph A. Maldjian, Kun Lu, Youngkyoo Jung, 2015, IEEE Transactions on Medical Imaging.

QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Metrics and Benchmarking Results pdf

Christos Davatzikos, Gowtham Krishnan Murugesan, Chandan Ganesh, 2021, ArXiv.

MEGnet: Automatic ICA-based artifact removal for MEG using spatiotemporal convolutional neural networks

Joseph A. Maldjian, Christopher T. Whitlow, Joel D. Stitzel, 2021, NeuroImage.

A novel fully automated MRI-based deep-learning method for classification of IDH mutation status in brain gliomas

Joseph A. Maldjian, Baowei Fei, Benjamin C. Wagner, 2019, bioRxiv.

Mapping of secondary somatosensory cortex activation induced by vibrational stimulation: an fMRI study

David C. Alsop, John A. Detre, Joseph A. Maldjian, 1999, Brain Research.

Semantic congruence is a critical factor in multisensory behavioral performance

Paul J. Laurienti, Jonathan H. Burdette, Joseph A. Maldjian, 2004, Experimental Brain Research.

A novel fully automated MRI-based deep-learning method for classification of 1p/19q co-deletion status in brain gliomas

Joseph A. Maldjian, Baowei Fei, Chandan Ganesh Bangalore Yogananda, 2020, bioRxiv.

Neural Correlates of Successful and Unsuccessful Verbal Memory Encoding

David C. Alsop, John A. Detre, Joseph A. Maldjian, 2002, Brain and Language.

Brain Atlas for Functional Imaging

Joseph A. Maldjian, J. Maldjian, 2002 .

MRI identification of early white matter injury in anoxic–ischemic encephalopathy

Joseph A. Maldjian, Ronald L. Wolf, Scott E. Kasner, 2001 .

A new high-dimensional machine learning approach for identifying Alzheimer Disease from MRI structural images

Ramon Casanova, Joseph A. Maldjian, Christopher T. Whitlow, 2011 .

The Sensory Somatotopic Map of the Human Hand Demonstrated at 4 Tesla

David C. Alsop, John A. Detre, Joseph A. Maldjian, 1999, NeuroImage.