| Retracted: A novel fully automated MRI-based deep-learning method for classification of IDH mutation status in brain gliomas CG Bangalore Yogananda, BR Shah, M Vejdani-Jahromi, SS Nalawade, ... Neuro-oncology 22 (3), 402-411, 2020 | 142 | 2020 |
| MRI-based deep-learning method for determining glioma MGMT promoter methylation status CGB Yogananda, BR Shah, SS Nalawade, GK Murugesan, FF Yu, ... American Journal of Neuroradiology 42 (5), 845-852, 2021 | 74 | 2021 |
| A fully automated deep learning network for brain tumor segmentation CGB Yogananda, BR Shah, M Vejdani-Jahromi, SS Nalawade, ... Tomography 6 (2), 186-193, 2020 | 68 | 2020 |
| A novel fully automated MRI-based deep-learning method for classification of 1p/19q co-deletion status in brain gliomas CGB Yogananda, BR Shah, FF Yu, MC Pinho, SS Nalawade, ... Neuro-oncology advances 2 (Supplement_4), iv42-iv48, 2020 | 51 | 2020 |
| QU-BraTS: MICCAI BraTS 2020 challenge on quantifying uncertainty in brain tumor segmentation-analysis of ranking scores and benchmarking results R Mehta, A Filos, U Baid, C Sako, R McKinley, M Rebsamen, K Dätwyler, ... The journal of machine learning for biomedical imaging 2022, 2022 | 37 | 2022 |
| Applications of resting state functional MR imaging to traumatic brain injury TJ O’Neill, EM Davenport, G Murugesan, A Montillo, JA Maldjian Neuroimaging Clinics 27 (4), 685-696, 2017 | 30 | 2017 |
| Classification of brain tumor isocitrate dehydrogenase status using MRI and deep learning S Nalawade, GK Murugesan, M Vejdani-Jahromi, RA Fisicaro, ... Journal of Medical Imaging 6 (4), 046003-046003, 2019 | 27 | 2019 |
| Biomedical image analysis competitions: The state of current participation practice M Eisenmann, A Reinke, V Weru, MD Tizabi, F Isensee, TJ Adler, ... arXiv preprint arXiv:2212.08568, 2022 | 23 | 2022 |
| Automatic 1D convolutional neural network-based detection of artifacts in MEG acquired without electrooculography or electrocardiography P Garg, E Davenport, G Murugesan, B Wagner, C Whitlow, J Maldjian, ... 2017 International Workshop on Pattern Recognition in Neuroimaging (PRNI), 1-4, 2017 | 23 | 2017 |
| A deep learning pipeline for automatic skull stripping and brain segmentation CGB Yogananda, BC Wagner, GK Murugesan, A Madhuranthakam, ... 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019 …, 2019 | 22 | 2019 |
| Multidimensional and multiresolution ensemble networks for brain tumor segmentation GK Murugesan, S Nalawade, C Ganesh, B Wagner, FF Yu, B Fei, ... Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries …, 2020 | 20 | 2020 |
| Fully automated brain tumor segmentation and survival prediction of gliomas using deep learning and MRI CG Bangalore Yogananda, B Wagner, SS Nalawade, GK Murugesan, ... International MICCAI Brainlesion Workshop, 99-112, 2020 | 20 | 2020 |
| MEGnet: Automatic ICA-based artifact removal for MEG using spatiotemporal convolutional neural networks AH Treacher, P Garg, E Davenport, R Godwin, A Proskovec, LG Bezerra, ... NeuroImage 241, 118402, 2021 | 18 | 2021 |
| Using convolutional neural networks to automatically detect eye-blink artifacts in magnetoencephalography without resorting to electrooculography P Garg, E Davenport, G Murugesan, B Wagner, C Whitlow, J Maldjian, ... Medical Image Computing and Computer Assisted Intervention− MICCAI 2017 …, 2017 | 17 | 2017 |
| 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 … B Saghafi, G Murugesan, E Davenport, B Wagner, J Urban, M Kelley, ... Medical Imaging 2018: Computer-Aided Diagnosis 10575, 90-98, 2018 | 15 | 2018 |
| 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 G Murugesan, B Saghafi, E Davenport, B Wagner, J Urban, M Kelley, ... Medical Imaging 2018: Computer-Aided Diagnosis 10575, 99-105, 2018 | 11 | 2018 |
| Head and neck primary tumor segmentation using deep neural networks and adaptive ensembling GK Murugesan, E Brunner, D McCrumb, J Kumar, J VanOss, S Moore, ... 3D Head and Neck Tumor Segmentation in PET/CT Challenge, 224-235, 2021 | 10 | 2021 |
| BrainNET: Inference of brain network topology using machine learning GK Murugesan, C Ganesh, S Nalawade, EM Davenport, B Wagner, ... Brain connectivity 10 (8), 422-435, 2020 | 10 | 2020 |
| Changes in resting state MRI networks from a single season of football distinguishes controls, low, and high head impact exposure G Murugesan, A Famili, E Davenport, B Wagner, J Urban, M Kelley, ... 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017 …, 2017 | 7 | 2017 |
| Automatic Whole Body FDG PET/CT Lesion Segmentation using Residual UNet and Adaptive Ensemble GK Murugesan, D McCrumb, E Brunner, J Kumar, R Soni, V Grigorash, ... bioRxiv, 2023.02. 06.525233, 2023 | 4 | 2023 |
