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Joseph A. Maldjian
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2020 – today
- 2024
- [j23]N. C. d. Fonseca, Jason Bowerman, Pegah Askari
, Amy L. Proskovec
Magnetoencephalography Atlas Viewer for Dipole Localization and Viewing. J. Imaging 10(4): 80 (2024) - [c17]Divya D. Reddy, Niloufar Saadat, James M. Holcomb, Benjamin C. Wagner, Nghi C. Truong, Jason Bowerman, Kimmo J. Hatanpaa, Toral R. Patel, Marco C. Pinho, Ananth J. Madhuranthakam, Chandan Ganesh Bangalore Yogananda, Joseph A. Maldjian:
Advancing Brain Tumor Analysis: Curating a High-Quality MRI Dataset for Deep Learning-Based Molecular Marker Profiling. CVPR Workshops 2024: 2373-2379 - 2021
- [j22]Alex Treacher
MEGnet: Automatic ICA-based artifact removal for MEG using spatiotemporal convolutional neural networks. NeuroImage 241: 118402 (2021) - [c16]Chandan Ganesh Bangalore Yogananda, Yudhajit Das, Benjamin C. Wagner, Sahil S. Nalawade
Disparity Autoencoders for Multi-class Brain Tumor Segmentation. BrainLes@MICCAI (2) 2021: 116-124 - [c15]Sahil S. Nalawade
Federated Learning for Brain Tumor Segmentation Using MRI and Transformers. BrainLes@MICCAI (2) 2021: 444-454 - [i1]Raghav Mehta, Angelos Filos, Ujjwal Baid, Chiharu Sako, Richard McKinley, Michael Rebsamen, Katrin Dätwyler, Raphael Meier, Piotr Radojewski, Gowtham Krishnan Murugesan, Sahil S. Nalawade, Chandan Ganesh, Benjamin C. Wagner, Fang F. Yu, Baowei Fei, Ananth J. Madhuranthakam, Joseph A. Maldjian, Laura Alexandra Daza, Catalina Gómez Caballero, Pablo Arbeláez, Chengliang Dai, Shuo Wang, Hadrien Raynaud, Yuanhan Mo, Elsa D. Angelini, Yike Guo, Wenjia Bai, Subhashis Banerjee, Linmin Pei, Murat Ak, Sarahi Rosas-González, Ilyess Zemmoura, Clovis Tauber, Minh H. Vu, Tufve Nyholm, Tommy Löfstedt, Laura Mora Ballestar, Verónica Vilaplana, Hugh McHugh, Gonzalo D. Maso Talou, Alan Wang, Jay B. Patel, Ken Chang, Katharina Hoebel, Mishka Gidwani, Nishanth Thumbavanam Arun, Sharut Gupta, Mehak Aggarwal, Praveer Singh, Elizabeth R. Gerstner, Jayashree Kalpathy-Cramer, Nicolas Boutry, Alexis Huard, Lasitha Vidyaratne, Md Monibor Rahman, Khan M. Iftekharuddin, Joseph Chazalon, Élodie Puybareau, Guillaume Tochon, Jun Ma, Mariano Cabezas, Xavier Lladó, Arnau Oliver, Liliana Valencia, Sergi Valverde, Mehdi Amian, Mohammadreza Soltaninejad, Andriy Myronenko, Ali Hatamizadeh, Xue Feng, Quan Dou, Nicholas J. Tustison, Craig H. Meyer, Nisarg A. Shah, Sanjay N. Talbar, Marc-André Weber, Abhishek Mahajan, András Jakab, Roland Wiest, Hassan M. Fathallah-Shaykh, Arash Nazeri, Mikhail Milchenko, Daniel S. Marcus, Aikaterini Kotrotsou, Rivka Colen, John B. Freymann, Justin S. Kirby, Christos Davatzikos, Bjoern H. Menze, Spyridon Bakas, Yarin Gal, Tal Arbel:
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Metrics and Benchmarking Results. CoRR abs/2112.10074 (2021) - 2020
- [j21]Debadatta Dash
MEG Sensor Selection for Neural Speech Decoding. IEEE Access 8: 182320-182337 (2020) - [j20]Gowtham Krishnan Murugesan
BrainNET: Inference of Brain Network Topology Using Machine Learning. Brain Connect. 10(8): 422-435 (2020) - [c14]Gowtham Krishnan Murugesan
Multidimensional and Multiresolution Ensemble Networks for Brain Tumor Segmentation. BrainLes@MICCAI (1) 2020: 448-457
2010 – 2019
- 2019
- [c13]Chandan Ganesh Bangalore Yogananda, Benjamin C. Wagner, Sahil S. Nalawade
Fully Automated Brain Tumor Segmentation and Survival Prediction of Gliomas Using Deep Learning and MRI. BrainLes@MICCAI (2) 2019: 99-112 - [c12]Gowtham Krishnan Murugesan
Multidimensional and Multiresolution Ensemble Networks for Brain Tumor Segmentation. BrainLes@MICCAI (2) 2019: 148-157 - [c11]Chandan Ganesh Bangalore Yogananda, Benjamin C. Wagner, Gowtham Krishnan Murugesan
A Deep Learning Pipeline for Automatic Skull Stripping and Brain Segmentation. ISBI 2019: 727-731 - 2018
- [c10]Debadatta Dash
Determining the Optimal Number of MEG Trials: A Machine Learning and Speech Decoding Perspective. BI 2018: 163-172 - [c9]Gowtham Krishnan Murugesan
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. Computer-Aided Diagnosis 2018: 105750F - [c8]Behrouz Saghafi, Gowtham Murugesan
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. Computer-Aided Diagnosis 2018: 105750E - 2017
- [j19]Joseph A. Maldjian
Erratum to: Multi-Atlas Library for Eliminating Normalization Failures in Non-Human Primates. Neuroinformatics 15(1): 111 (2017) - [c7]Gowtham Murugesan
Changes in resting state MRI networks from a single season of football distinguishes controls, low, and high head impact exposure. ISBI 2017: 464-467 - [c6]Behrouz Saghafi, Prabhat Garg, Benjamin C. Wagner, S. Carrie Smith, Jianzhao Xu, Ananth J. Madhuranthakam, Youngkyoo Jung
Quantifying the Impact of Type 2 Diabetes on Brain Perfusion Using Deep Neural Networks. DLMIA/ML-CDS@MICCAI 2017: 151-159 - [c5]Prabhat Garg, Elizabeth M. Davenport
Using Convolutional Neural Networks to Automatically Detect Eye-Blink Artifacts in Magnetoencephalography Without Resorting to Electrooculography. MICCAI (3) 2017: 374-381 - [c4]Prabhat Garg, Elizabeth M. Davenport
Automatic 1D convolutional neural network-based detection of artifacts in MEG acquired without electrooculography or electrocardiography. PRNI 2017: 1-4 - 2016
- [j18]Joseph A. Maldjian
Multi-Atlas Library for Eliminating Normalization Failures in Non-Human Primates. Neuroinformatics 14(2): 183-190 (2016) - 2015
- [j17]Ashley A. Weaver
Image segmentation and registration algorithm to collect thoracic skeleton semilandmarks for characterization of age and sex-based thoracic morphology variation. Comput. Biol. Medicine 67: 41-48 (2015) - [j16]Megan E. Johnston, Kun Lu
Multi-TI Arterial Spin Labeling MRI with Variable TR and Bolus Duration for Cerebral Blood Flow and Arterial Transit Time Mapping. IEEE Trans. Medical Imaging 34(6): 1392-1402 (2015) - 2014
- [j15]Sriraam Natarajan, Baidya Nath Saha, Saket Joshi, Adam Edwards
Relational learning helps in three-way classification of Alzheimer patients from structural magnetic resonance images of the brain. Int. J. Mach. Learn. Cybern. 5(5): 659-669 (2014) - [j14]Joseph A. Maldjian
Graph theoretical analysis of resting-state MEG data: Identifying interhemispheric connectivity and the default mode. NeuroImage 96: 88-94 (2014) - [j13]Joseph A. Maldjian
Vervet MRI Atlas and Label Map for Fully Automated Morphometric Analyses. Neuroinformatics 12(4): 543-550 (2014) - 2013
- [c3]Baidya Nath Saha, Gautam Kunapuli, Nilanjan Ray, Joseph A. Maldjian
AR-Boost: Reducing Overfitting by a Robust Data-Driven Regularization Strategy. ECML/PKDD (3) 2013: 1-16 - 2012
- [c2]Baidya Nath Saha, Sriraam Natarajan, Gopi Kota, Christopher T. Whitlow, Donald W. Bowden, Jasmin Divers, Barry I. Freedman, Joseph A. Maldjian
A Novel Hierarchical Level Set with AR-boost for White Matter Lesion Segmentation in Diabetes. ICMLA (1) 2012: 90-95 - [c1]Sriraam Natarajan, Saket Joshi, Baidya Nath Saha, Adam Edwards
A Machine Learning Pipeline for Three-Way Classification of Alzheimer Patients from Structural Magnetic Resonance Images of the Brain. ICMLA (1) 2012: 203-208 - 2011
- [j12]Ramon Casanova, Christopher T. Whitlow, Benjamin C. Wagner, Jeff Williamson, Sally A. Shumaker, Joseph A. Maldjian
High Dimensional Classification of Structural MRI Alzheimer's Disease Data Based on Large Scale Regularization. Frontiers Neuroinformatics 5: 22 (2011)
2000 – 2009
- 2009
- [j11]W. David Hairston
An adaptive staircase procedure for the E-Prime programming environment. Comput. Methods Programs Biomed. 93(1): 104-108 (2009) - [j10]Joseph A. Maldjian
Fully Automated Processing of fMRI Data in SPM: from MRI Scanner to PACS. Neuroinformatics 7(1): 57-72 (2009) - 2008
- [j9]Ann M. Peiffer, Joseph A. Maldjian
Resurrecting Brinley Plots for a Novel Use: Meta-Analyses of Functional Brain Imaging Data in Older Adults. Int. J. Biomed. Imaging 2008: 167078:1-167078:7 (2008) - [j8]Ramon Casanova, Srikanth Ryali
The impact of temporal regularization on estimates of the BOLD hemodynamic response function: A comparative analysis. NeuroImage 40(4): 1606-1618 (2008) - 2007
- [j7]Ramon Casanova, Ryali Srikanth
Biological parametric mapping: A statistical toolbox for multimodality brain image analysis. NeuroImage 34(1): 137-143 (2007) - 2006
- [j6]Srikanth Ryali
Estimation of false discovery rates for wavelet-denoised statistical parametric maps. NeuroImage 33(1): 72-84 (2006) - 2004
- [j5]Joseph A. Maldjian
Precentral gyrus discrepancy in electronic versions of the Talairach atlas. NeuroImage 21(1): 450-455 (2004) - 2003
- [j4]Joseph A. Maldjian
An automated method for neuroanatomic and cytoarchitectonic atlas-based interrogation of fMRI data sets. NeuroImage 19(3): 1233-1239 (2003) - 2002
- [j3]Daniel D. Langleben, Lee Schroeder, Joseph A. Maldjian
Brain Activity during Simulated Deception: An Event-Related Functional Magnetic Resonance Study. NeuroImage 15(3): 727-732 (2002) - [j2]Ruben C. Gur, Lee Schroeder, Travis Turner, Claire McGrath, Robin M. Chan, Bruce I. Turetsky, David C. Alsop
Brain Activation during Facial Emotion Processing. NeuroImage 16(3): 651-662 (2002) - [j1]Paul J. Laurienti, Aaron S. Field, Jonathan H. Burdette, Joseph A. Maldjian, Yi-Fen Yen, Dixon M. Moody:
Dietary Caffeine Consumption Modulates fMRI Measures. NeuroImage 17(2): 751-757 (2002)
Coauthor Index
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last updated on 2024-11-28 21:25 CET by the dblp team
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