Author

D. R. Wong

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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Pathology· 2 Pharmacology & Toxicology· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 3 Research Focus:cell type annotation· 2 Research Focus:differential expression· 2 Organism:human· 2 Assay:imaging· 2 Research Focus:cell-cell communication· 1 Research Focus:clustering· 1 Research Focus:drug response / drug discovery· 1

Frequent Co-Authors

M. J. Keiser· 3 shared A. J. Butte· 2 shared B. N. Dugger· 2 shared A. C. Martini· 1 shared A. Laeremans· 1 shared C. DeCarli· 1 shared C. K. Williams· 1 shared C. Khacherian· 1 shared

Activity by Year

2021-2023 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 26 mutual co-author links

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3 shared papersPathology

M. J. Keiser

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Author profile →

3 papers

Recent·Most cited

Learning fast and fine-grained detection of amyloid neuropathologies from coarse-grained expert labels

D. R. Wong, S. D. Magaki, H. V. Vinters, W. H. Yong, E. S. Monuki, C. K. Williams, A. C. Martini, C. DeCarli, C. Khacherian, J. P. Graff, B. N. Dugger, M. J. Keiser

The paper develops a deep learning model to detect amyloid neuropathologies from whole slide images using coarse labels, achieving performance comparable to expert annotations.

bioRxiv · Jan 13, 2023Pathology

Deep learning from multiple experts improves identification of amyloid neuropathologies

D. R. Wong, Z. Tang, N. C. Mew, S. Das, J. Athey, K. E. McAleese, J. K. Kofler, M. E. Flanagan, E. Borys, C. L. White, A. J. Butte, B. N. Dugger, M. J. Keiser

The paper develops a deep learning model that combines multiple expert annotations to improve the identification of amyloid neuropathologies, achieving consistent diagnoses across varying expert opinions.

bioRxiv · Mar 12, 2021Pathology

Trans-channel fluorescence learning improves high-content screening for Alzheimer's disease therapeutics

D. R. Wong, J. Conrad, N. Johnson, J. I. Ayers, A. Laeremans, J. C. Lee, J. Lee, S. B. Prusiner, S. Bandyopadhyay, A. J. Butte, N. A. Paras, M. J. Keiser

This paper introduces a deep learning method to improve high-content screening for Alzheimer's disease therapeutics by generating predicted fluorescent signals from alternative markers.

bioRxiv · Jan 8, 2021Pharmacology & Toxicology