Author

Sina Ghandian

University of California, San FranciscoORCID 0009-0004-2312-0950Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Pathology· 2

Signature Assays, Organisms & Methods

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Research Focus:cell type annotation· 2 Approach:deep learning· 2 Organism:human· 2 Assay:imaging· 2

Frequent Co-Authors

Michael J. KeiseriD· 2 shared Andrew F. TeichiD· 1 shared Brittany N. DuggeriD· 1 shared Charles S DeCarliiD· 1 shared Elizabeth Keiser· 1 shared Garrett GaskinsiD· 1 shared Kiana NavaiD· 1 shared Laurel A. BeckettiD· 1 shared

Activity by Year

2025-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 31 mutual co-author links

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2 shared papersPathology

Michael J. KeiserORCID

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2 papers

Recent·Most cited

PathologyScientific ReportsPeer-reviewedAug 28, 2026

Learning precise segmentation of neurofibrillary tangles from rapid manual point annotations

Sina Ghandian, Liane Albarghouthi, Kiana Nava…·Michael J. Keiser

This paper develops a deep-learning method to segment neurofibrillary tangles from brain tissue images using manual annotations, achieving high accuracy and scalability.

4 citations

humanimaging

𝕏 PostBluesky

PathologyCell Reports MethodsPeer-reviewedOct 1, 2025

Machine-learning convergent melanocytic morphology despite noisy archival slides

Mikio Tada, Garrett Gaskins, Sina Ghandian…·Elizabeth Keiser

This study develops deep-learning models to detect melanocytic atypia from H&E slides using complementary IHC stains, achieving high AUROC and AUPRC scores despite limited paired tissue data.

1 citation

humanimaging

𝕏 PostBluesky