Author

G. K. Murugesan

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioengineering· 3

Signature Assays, Organisms & Methods

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Approach:deep learning· 3 Research Focus:clustering· 2 Assay:imaging· 2 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Assay:field survey / ecological data· 1 Organism:human· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

A. Chang· 2 shared D. McCrumb· 2 shared E. Brunner· 2 shared J. Kumar· 2 shared J. VanOss· 2 shared R. Soni· 2 shared S. Moore· 2 shared V. Grigorash· 2 shared

Activity by Year

2020-2023 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 36 mutual co-author links

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2 shared papersBioengineering

A. Chang

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3 papers

Recent·Most cited

Automatic Abdominal Multi Organ Segmentationusing Residual UNet

G. K. Murugesan, D. McCrumb, E. Brunner, J. Kumar, R. Soni, V. Grigorash, A. Chang, A. Peck, J. VanOss, S. Moore

This paper develops a residual UNet model for abdominal multi-organ segmentation using CT and MRI scans, achieving high Dice scores in held-out tests.

bioRxiv · Feb 15, 2023 · PreprintBioengineering

Automatic Whole Body FDG PET/CT Lesion Segmentation using Residual UNet and Adaptive Ensemble

G. K. Murugesan, D. Mccrumb, E. Brunner, J. Kumar, R. Soni, V. Grigorash, A. Chang, J. VanOss, S. Moore

The paper develops a deep learning method using residual UNET and adaptive ensemble for automated FDG PET/CT lesion segmentation, achieving a dice score of 0.5541 on a test dataset.

bioRxiv · Feb 6, 2023 · PreprintBioengineering

BrainNET: Inference of brain network topology using Machine Learning

G. K. Murugesan, S. Nalawade, C. G. Bangalore Yogananda, E. Davenport, B. Wagner, W. H. Kim, J. A. Maldjian

BrainNET, a machine learning method for fMRI network inference, outperforms correlation and partial correlation in identifying brain network changes in ADHD.

bioRxiv · Feb 16, 2020Bioengineering