Author
The University of Texas at DallasORCID 0000-0002-9123-9484Unclaimed ORCID profile · follow to get notified of new preprints
Mutual co-authors in cluster
8 papers
Recent·Most cited
Alireza Saberigarakani, Riya P. Patel, Milad Almasian, Xinyuan Zhang, Jonathan Brewer, Sohail S. Hassan, Jichen Chai, Juhyun Lee, Baowei Fei, Jie Yuan, Kelli Carroll, Yichen Ding
The paper develops a framework for real-time volumetric imaging of zebrafish hearts to study contractile dynamics at the cellular level.
Cell Reports Methods · Jul 23, 2025Peer-reviewedBioengineering
Chandan Ganesh Bangalore Yogananda, Bhavya Shah, Sahil Sunil Nalawade, Gowtham Krishnan Murugesan, Fang Frank Yu, Marco C. Pinho, Benjamin Wagner, Bruce Mickey, Toral Patel, Baowei Fei, Ananth J. Madhuranthakam, Joseph A. Maldjian
This paper develops a deep learning model using T2-w MRI to predict MGMT promoter methylation in gliomas, achieving higher accuracy than histological methods.
American Journal of Neuroradiology · Mar 4, 2021Peer-reviewedNeuroscience
Chandan Ganesh Bangalore Yogananda, Bhavya R Shah, Frank F. Yu, Marco C. Pinho, Sahil Sunil Nalawade, Gowtham Krishnan Murugesan, Benjamin Wagner, Bruce Mickey, Toral Patel, Baowei Fei, Ananth J. Madhuranthakam, Joseph A. Maldjian
This paper develops a deep learning model using T2-weighted MRI to classify 1p/19q co-deletion status in brain gliomas with high accuracy.
Neuro-Oncology Advances · Aug 14, 2020Peer-reviewedNeuroscience
Sahil Sunil Nalawade, Fang Frank Yu, Chandan Ganesh Bangalore Yogananda, Gowtham Krishnan Murugesan, Bhavya Shah, Marco C. Pinho, Benjamin Wagner, Bruce Mickey, Toral Patel, Baowei Fei, Ananth J. Madhuranthakam, Joseph A. Maldjian
This study evaluates motion artifact effects on glioma marker classification using deep learning and develops a motion correction network to restore classification accuracy.
bioRxiv · Jun 1, 2020 · PreprintNeuroscience
Chandan Ganesh Bangalore Yogananda, Ben C. Wagner, Sahil Sunil Nalawade, Gowtham Krishnan Murugesan, Marco C. Pinho, Baowei Fei, Ananth J. Madhuranthakam, Joseph A. Maldjian
This paper develops a deep learning method for automated brain tumor segmentation and survival prediction using MRI data from the BraTS2019 dataset, achieving high segmentation accuracy and moderate survival prediction performance.
Lecture notes in computer science · Jan 1, 2020Peer-reviewedNeuroscience
Chandan Ganesh Bangalore Yogananda, Bhavya Shah, Maryam Vejdani‐Jahromi, Sahil Sunil Nalawade, Gowtham Krishnan Murugesan, Frank F. Yu, Marco C. Pinho, Benjamin Wagner, Bruce Mickey, Toral Patel, Baowei Fei, Ananth J. Madhuranthakam, Joseph A. Maldjian
The paper withdraws its manuscript due to errors in analysis and requests it not be cited.
Neuro-Oncology · Oct 17, 2019Peer-reviewedNeuroscience
Gowtham Krishnan Murugesan, Sahil Sunil Nalawade, Chandan Ganesh Bangalore Yogananda, Ben C. Wagner, Fang Frank Yu, Baowei Fei, Ananth J. Madhuranthakam, Joseph A. Maldjian
The paper develops and ensembles 2D/3D segmentation models for brain tumors using multiresolution inputs, achieving high DICE scores and survival prediction accuracy on BRATS 2019 data.
bioRxiv · Sep 10, 2019 · PreprintNeuroscience
Sahil Sunil Nalawade, Gowtham Krishnan Murugesan, Maryam Vejdani‐Jahromi, Ryan A. Fisicaro, Chandan Ganesh Bangalore Yogananda, Ben C. Wagner, Bruce Mickey, Elizabeth Anne Maher, Marco C. Pinho, Baowei Fei, Ananth J. Madhuranthakam, Joseph A. Maldjian
This paper develops a deep learning model to predict IDH mutation status from T2-weighted MRI scans of brain tumors, achieving 83.8% test accuracy.
bioRxiv · Sep 4, 2019 · PreprintNeuroscience