Author

M. A. Anastasio

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· 5

Signature Assays, Organisms & Methods

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Approach:deep learning· 5 Assay:imaging· 4 Research Focus:cell type annotation· 3 Organism:human· 2 Organism:mouse· 2 Organism:chicken / avian· 1 Research Focus:clustering· 1 Assay:field survey / ecological data· 1

Frequent Co-Authors

X. Chen· 3 shared G. Popescu· 2 shared M. Lowerison· 2 shared P. Song· 2 shared Q. You· 2 shared Y. Shin· 2 shared Z. Dong· 2 shared A. M. Svoboda· 1 shared

Activity by Year

2020-2023 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 19 mutual co-author links

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3 shared papersBioengineering

X. Chen

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Author profile →

5 papers

Recent·Most cited

Identifying Naturalistic Movies from Human Brain Activity with High-Density Diffuse Optical Tomography

Z. E. Markow, K. Tripathy, A. M. Svoboda, M. L. Schroeder, S. M. Rafferty, E. J. Richter, A. T. Eggebrecht, M. A. Anastasio, M. A. Chevillet, E. M. Mugler, S. N. Naufel, A. Yin, J. W. Trobaugh, J. P. Culver

This study uses HD-DOT to decode naturalistic movie content from brain activity with accuracy above chance, demonstrating its potential for non-invasive, real-world applications.

bioRxiv · Nov 27, 2023 · PreprintBioengineering

Machine learning assisted health viability assay for mouse embryos with artificial confocal microscopy (ACM)

N. Goswami, N. Winston, W. Choi, N. Z. E. Lai, R. B. Arcanjo, X. Chen, N. Sobh, R. A. Nowak, M. A. Anastasio, G. Popescu

The paper develops a machine learning tool using artificial confocal microscopy to assess embryo viability by detecting nucleus features and classifying embryos into healthy/intermediate or sick categories.

bioRxiv · Jul 30, 2023 · PreprintBioengineering

Context-Aware Deep Learning Enables High-Efficacy Localization of High Concentration Microbubbles for Super-Resolution Ultrasound Localization Microscopy

Y. Shin, M. Lowerison, Y. Wang, X. Chen, Q. You, Z. Dong, M. A. Anastasio, P. Song

This paper introduces LOCA-ULM, a deep learning method for ultrasound localization microscopy that improves microbubble detection and localization accuracy in vivo and in silico.

bioRxiv · Apr 21, 2023Bioengineering

Contrast-free Super-resolution Doppler (CS Doppler) based on Deep Generative Neural Networks

Q. You, M. Lowerison, Y. Shin, X. Chen, N. V. Chandra Sekaran, Z. Dong, D. A. Llano, M. A. Anastasio, P. Song

This paper introduces CS Doppler, a contrast-free super-resolution Doppler technique using deep generative networks, achieving improved spatial resolution and real-time imaging in vivo.

bioRxiv · Sep 29, 2022 · PreprintBioengineering

Live-dead assay on unlabeled cells using phase imaging with computational specificity

C. Hu, S. He, Y. J. Lee, Y. He, E. M. Kong, H. Li, M. A. Anastasio, G. Popescu

This paper introduces a method to assess cell viability without staining using phase imaging and deep learning, achieving 95% accuracy in distinguishing live and dead cells.

bioRxiv · Oct 28, 2020Bioengineering