Author

A. A. Oberai

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· 2 Biophysics· 1 Cancer Biology· 1

Signature Assays, Organisms & Methods

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Assay:imaging· 4 Approach:deep learning· 3 Research Focus:cell type annotation· 2 Research Focus:benchmarking· 1 Research Focus:cell signaling & trafficking· 1 Research Focus:clustering· 1 Approach:contrastive learning· 1 Research Focus:dimensionality reduction· 1

Frequent Co-Authors

D. Ray· 2 shared A. Mowla· 1 shared B. F. Kennedy· 1 shared B. Shaddy· 1 shared B. Varghese· 1 shared D. Hwang· 1 shared D. Pelletier· 1 shared D. Song· 1 shared

Activity by Year

2019-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 11 mutual co-author links

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2 shared papersCancer Biology

D. Ray

Mutual co-authors in cluster

Author profile →

4 papers

Recent·Most cited

Tumor spheroid elasticity estimation using mechano-microscopy combined with a conditional generative adversarial network

K. Y. Foo, B. Shaddy, J. Murgoitio-Esandi, M. S. Hepburn, J. Li, A. Mowla, D. Vahala, S. E. Amos, Y. S. Choi, A. A. Oberai, B. F. Kennedy

This study trains a conditional generative adversarial network to estimate tumor spheroid elasticity from phase difference images, showing improved robustness to noise compared to traditional methods.

bioRxiv · Apr 12, 2024Bioengineering

Conditional Generative Learning for Medical Image Imputation

R. Raad, D. Ray, B. Varghese, D. Hwang, I. Gill, V. Duddalwar, A. A. Oberai

This paper develops a generative algorithm for medical image imputation, showing it outperforms deterministic deep learning methods and provides confidence measures for imputed images.

bioRxiv · Apr 3, 2023 · PreprintCancer Biology

Probabilistic Brain Extraction in MR Images via Conditional Generative Adversarial Networks

S. Moazami, D. Ray, D. Pelletier, A. A. Oberai

This paper introduces a probabilistic deep learning method for brain extraction in MR images using cGANs, showing improved accuracy and uncertainty quantification compared to existing tools.

bioRxiv · Mar 14, 2022Bioengineering

Three-dimensional traction microscopy accounting for cell-induced matrix degradation

D. Song, D. T. Seidl, A. A. Oberai

This paper introduces a computational method to quantify both cellular tractions and ECM degradation in 3D Traction Microscopy, showing that neglecting degradation leads to significant traction measurement errors.

bioRxiv · Nov 15, 2019Biophysics