Author

E. Nehme

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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Biophysics· 2 Bioengineering· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 3 Assay:imaging· 3 Approach:statistical model· 2 Research Focus:benchmarking· 1 Research Focus:cell signaling & trafficking· 1 Research Focus:spatial domain detection· 1

Frequent Co-Authors

Y. Shechtman· 3 shared L. E. Weiss· 2 shared O. Alalouf· 2 shared A. Parizat· 1 shared B. Ferdman· 1 shared D. Xiao· 1 shared E. Perlson· 1 shared M. Chein· 1 shared

Activity by Year

2019-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 25 mutual co-author links

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3 shared papersBiophysics

Y. Shechtman

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3 papers

Recent·Most cited

Large-FOV 3D localization microscopy by spatially variant point spread function generation

D. Xiao, R. Kedem Orange, N. Opatovski, A. Parizat, E. Nehme, O. Alalouf, Y. Shechtman

This paper introduces PPG3D, a PCA-based PSF generator for 3D localization microscopy, improving accuracy and speed for large FOV imaging.

bioRxiv · Jul 30, 2023 · PreprintBioengineering

VIPR: Vectorial Implementation of Phase Retrieval for fast and accurate microscopic pixel-wise pupil estimation

B. Ferdman, E. Nehme, L. E. Weiss, R. Orange, O. Alalouf, Y. Shechtman

The paper introduces a fast and accurate method for estimating microscopic pixel-wise pupil phase information using a vectorial diffraction model, without prior knowledge.

bioRxiv · Jan 2, 2020Biophysics

Single particle diffusion characterization by deep learning

N. Granik, E. Nehme, L. E. Weiss, M. Levin, M. Chein, E. Perlson, Y. Roichman, Y. Shechtman

This paper develops a deep learning method to classify and quantify anomalous diffusion processes from single-particle trajectories, outperforming traditional methods with fewer data points.

bioRxiv · Apr 4, 2019Biophysics