Author

M. Moyle

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· 1 Biophysics· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Assay:fluorescence / live-cell microscopy· 2 Organism:C. elegans· 1 Research Focus:deconvolution· 1 Research Focus:dimensionality reduction· 1 Research Focus:imputation· 1 Organism:mouse· 1

Frequent Co-Authors

D. Colon-Ramos· 2 shared H. Liu· 2 shared H. Shroff· 2 shared J. Liu· 2 shared M. Guo· 2 shared R. Christensen· 2 shared X. Han· 2 shared Y. Su· 2 shared

Activity by Year

2022-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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2 shared papersBioengineering

D. Colon-Ramos

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Author profile →

2 papers

Recent·Most cited

Deep learning-based aberration compensation improves contrast and resolution in fluorescence microscopy

M. Guo, Y. Wu, C. M. Hobson, Y. Su, S. Qian, E. Krueger, R. Christensen, G. Kroeschell, J. Bui, M. Chaw, L. Zhang, J. Liu, X. Hou, X. Han, Z. Lu, , H. Shroff

This paper introduces a deep learning method to correct optical aberrations in fluorescence microscopy, improving image quality and enabling better downstream analysis.

bioRxiv · Oct 15, 2023 · PreprintBioengineering

Incorporating the image formation process into deep learning improves network performance in deconvolution applications

Y. Li, Y. Su, M. Guo, X. Han, J. Liu, H. Vishwasrao, X. Li, R. Christensen, T. Sengupta, M. Moyle, J. Chen, T. B. Usdin, D. Colon-Ramos, H. Liu, Y. Wu, H. Shroff

This paper introduces RLN, a deep learning method for 3D fluorescence microscopy deconvolution that improves speed, performance, and generalizability compared to existing methods.

bioRxiv · Mar 5, 2022 · PreprintBiophysics