Author

Won-Dong Jang

Harvard UniversityORCID 0000-0002-5205-1024Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Organism:human· 2 Assay:imaging· 2 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Approach:machine learning (classical)· 1 Research Focus:spatial domain detection· 1

Frequent Co-Authors

Hanspeter PfisteriD· 2 shared Peter Karl SorgeriD· 2 shared Artem SokoloviD· 1 shared Clarence YappiD· 1 shared Connor A. JacobsoniD· 1 shared Donglai WeiiD· 1 shared Edward NovikoviD· 1 shared Johanna BeyeriD· 1 shared

Activity by Year

2019-2022 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 30 mutual co-author links

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2 shared papersBioinformatics

Hanspeter PfisterORCID

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2 papers

Recent·Most cited

UnMICST: Deep learning with real augmentation for robust segmentation of highly multiplexed images of human tissues

Clarence Yapp, Edward Novikov, Won-Dong Jang, Tuulia Vallius, Yuan Chen, Marcelo Cicconet, Zoltan Maliga, Connor A. Jacobson, Donglai Wei, Sandro Santagata, Hanspeter Pfister, Peter Karl Sorger

The paper introduces two methods to improve tissue segmentation in highly multiplexed images using real augmentations and nuclear envelope imaging, resulting in more accurate models.

Communications Biology · Nov 18, 2022Peer-reviewedBioinformatics

Facetto: Combining Unsupervised and Supervised Learning for Hierarchical Phenotype Analysis in Multi-Channel Image Data

Robert Krueger, Johanna Beyer, Won-Dong Jang, Nam Wook Kim, Artem Sokolov, Peter Karl Sorger, Hanspeter Pfister

Facetto combines unsupervised and supervised learning to analyze multi-channel microscopy images, enabling discovery of cell types and phenotypes in cancer tissues.

IEEE Transactions on Visualization and Computer Graphics · Sep 11, 2019Peer-reviewedSystems Biology