Author

Ketaki Buwa

University of Southern California

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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Neuroscience· 2

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Approach:deep learning· 2 Research Focus:differential expression· 2 Organism:human· 2 Assay:imaging· 2 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Approach:contrastive learning· 1 Research Focus:dimensionality reduction· 1

Frequent Co-Authors

Paul M. ThompsoniD· 2 shared Saket S. Ozarkar· 2 shared Sophia I. ThomopoulosiD· 2 shared Tamoghna ChattopadhyayiD· 2 shared Emily Laltoo· 1 shared Ganesan VenkatasubramanianiD· 1 shared Himanshu JoshiiD· 1 shared John P. JohniD· 1 shared

Activity by Year

2023-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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2 shared papersNeuroscience

Paul M. ThompsonORCID

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Author profile →

2 papers

Recent·Most cited

Brain Age Analysis and Dementia Classification using Convolutional Neural Networks trained on Diffusion MRI: Tests in Indian and North American Cohorts

Tamoghna Chattopadhyay, Neha Ann Joshy, Saket S. Ozarkar, Ketaki Buwa, Yixue Feng, Emily Laltoo, Sophia I. Thomopoulos, Julio E. Villalon, Himanshu Joshi, Ganesan Venkatasubramanian, John P. John, Paul M. Thompson

This paper evaluates CNN models for brain age prediction and dementia classification using diffusion MRI, showing improved performance after dataset harmonization across Indian and North American cohorts.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · Jul 15, 2024Peer-reviewedNeuroscience

Predicting Brain Amyloid Positivity from T1 weighted brain MRI and MRI-derived Gray Matter, White Matter and CSF maps using Transfer Learning on 3D CNNs

Tamoghna Chattopadhyay, Saket S. Ozarkar, Ketaki Buwa, Sophia I. Thomopoulos, Paul M. Thompson, the Alzheimer’s Disease Neuroimaging Initiative

This paper predicts brain amyloid positivity from T1-weighted MRI and derived tissue maps using 3D CNNs and transfer learning, achieving 76% balanced accuracy.

bioRxiv · Feb 15, 2023 · PreprintNeuroscience