Author

A. A. Suriawinata

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· 3

Signature Assays, Organisms & Methods

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Organism:human· 3 Assay:imaging· 3 Research Focus:benchmarking· 2 Research Focus:cell type annotation· 2 Approach:deep learning· 2 Research Focus:clustering· 1 Approach:contrastive learning· 1 Research Focus:differential expression· 1

Frequent Co-Authors

J. Levy· 3 shared L. J. Vaickus· 3 shared X. Liu· 3 shared B. Christensen· 2 shared B. Ren· 2 shared C. A. Bobak· 2 shared M. Lisovsky· 2 shared N. Azizgolshani· 2 shared

Activity by Year

2020-2022 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 35 mutual co-author links

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3 shared papersPathology

J. Levy

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3 papers

Recent·Most cited

Uncovering Additional Predictors of Urothelial Carcinoma from Voided Urothelial Cell Clusters Through a Deep Learning Based Image Preprocessing Technique

J. Levy, X. Liu, J. D. Marotti, D. A. Kerr, E. J. Gutmann, R. Glass, C. P. Dodge, A. A. Suriawinata, L. J. Vaickus

This paper develops a deep learning-based preprocessing tool to analyze urothelial cell clusters in urine cytology, identifying key markers of atypia that could improve machine learning applications in bladder cancer detection.

bioRxiv · Apr 30, 2022Pathology

Improving the Virtual Trichrome Assessment through Bridge Category Models

J. Levy, C. A. Bobak, N. Azizgolshani, X. Liu, B. Ren, M. Lisovsky, A. A. Suriawinata, B. Christensen, J. O'Malley, L. J. Vaickus

This paper assesses Virtual Trichrome technologies using Bridge Category Models to account for bridge ratings in Fibrosis staging of NASH tissue.

bioRxiv · Oct 30, 2021 · PreprintPathology

A Large-Scale Internal Validation Study of Unsupervised Virtual Trichrome Staining Technologies on Non-alcoholic Steatohepatitis Liver Biopsies

J. Levy, N. Azizgolshani, M. Andersen, A. A. Suriawinata, X. Liu, M. Lisovsky, B. Ren, C. A. Bobak, B. Christensen, L. J. Vaickus

This study validates the use of GANs for virtual trichrome staining of NASH liver biopsies, showing high correlation with real stains and clinical outcomes.

bioRxiv · Jul 3, 2020Pathology