Faculty profile

Ali Bashashati

ResearcherPathology and Laboratory MedicineUniversity of British Columbia

This profile is built from public research funding records (CIHR, NSERC and SSHRC) and PubMed. We have not imported them from a University of British Columbia directory, so their courses may be missing. Find their university profile.

Ali Bashashati

Researcher

Pathology and Laboratory Medicine

Health Sciences

University of British Columbia

Vancouver, British Columbia, Canada

Supervision signals

Has led research grants, which requires an independent research appointment. Ask their department whether they supervise graduate students.
20 publications in the last five years, latest in 2026.
Senior (last) author on 10 recent papers, a sign of leading a research group.
Principal investigator on 5 research grants (3 CIHR, 2 NSERC), most recently in 2024.
Supervised 3 trainees who won CIHR awards, most recently in 2025.

These signals come from public records, including CIHR, NSERC and SSHRC funding decisions. Ask the professor or the graduate program whether they are taking new students.

Academic metrics

Publications

Citations

h-index

Collaborators

Grants

Publications by year

Counts cover the works listed on the university profile; citation counts come from Dimensions.

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Signals and details

Research

Digital Pathology Computational Pathology Machine Learning Image Analysis Prostate Cancer Transcriptomics Artificial Intelligence Biomarkers

Latest papers

Regional tissue perfusion index (RTPI): a new optical-based metric for quantifying regional tissue perfusion.
Journal of clinical monitoring and computing · 2026 · senior author
Recommendations for Pathologist-Led Deployment of Artificial Intelligence Tools as Laboratory-Developed Tests.
Laboratory investigation; a journal of technical methods and pathology · 2026
Performance Assessment of a Deep Learning-based Algorithm for Ovarian Cancer Histotyping in an Independent Data Set.
The American journal of surgical pathology · 2026 · senior author

All 74 publications

Latest funding

Stimulated Raman Histology for Real-Time Identification and Risk Stratification of Prostate Cancer
CIHR · 2025 · Co-investigator
$887,400
Digital Pathology and AI-Driven Biomarker Discovery to Personalize Bevacizumab Therapy in Ovarian Cancer
CIHR · 2025 · Supervisor
$120,000
Artificial Intelligence-Driven Computational Pathology Platform For Bladder Cancer Sub-typing
CIHR · 2024 · Supervisor
$120,000

All 12 grants and awards

74 publications.

Regional tissue perfusion index (RTPI): a new optical-based metric for quantifying regional tissue perfusion.

Shadgan B, Amani Tehrani I, Khosravi S, Askari Z, Pirhadi Rad AP, Bashashati A

Journal of clinical monitoring and computing2026Senior author

DOI: 10.1007/s10877-026-01424-0 PMID: 41733851

Recommendations for Pathologist-Led Deployment of Artificial Intelligence Tools as Laboratory-Developed Tests.

Zhang AW, Cecchini MJ, Bashashati A, Gilks CB, Kinloch M, Schaeffer DF

Laboratory investigation; a journal of technical methods and pathology2026

DOI: 10.1016/j.labinv.2026.106131 PMID: 42067141

Performance Assessment of a Deep Learning-based Algorithm for Ovarian Cancer Histotyping in an Independent Data Set.

Zelisse HS, Asadi-Aghbolaghi M, Farahani H, Snijders MLH, Hooijer GKJ, Mom CH, van Gent MDJM, Dijk F, Horlings HM, van de Vijver MJ, Bashashati A

The American journal of surgical pathology2026Senior author

DOI: 10.1097/pas.0000000000002481 PMID: 41251420

A Deep Learning Framework for Classification of Neuroendocrine Neoplasm Whole Slide Images.

Hadjifaradji A, Diaz-Stewart M, Chu J, Farnell D, Schaeffer D, Farahani H, Bashashati A, Loree JM

Cancers2025

DOI: 10.3390/cancers17182991 PMID: 41008835

Benchmarking histopathology foundation models for ovarian cancer bevacizumab treatment response prediction from whole slide images.

Mallya M, Mirabadi AK, Farnell D, Farahani H, Bashashati A

Discover oncology2025Senior author

DOI: 10.1007/s12672-025-01973-x PMID: 39961889

ATEC23 Challenge: Automated prediction of treatment effectiveness in ovarian cancer using histopathological images.

Wang CW, Firdi NP, Chu TC, Faiz MFI, Iqbal MZ, Li Y, Yang B, Mallya M, Bashashati A, Li F, Wang H, Lu M, Xia Y, Chao TK

Medical image analysis2025

DOI: 10.1016/j.media.2024.103342 PMID: 39260034

A Deep Learning Approach for the Identification of the Molecular Subtypes of Pancreatic Ductal Adenocarcinoma Based on Whole Slide Pathology Images.

Ahmadvand P, Farahani H, Farnell D, Darbandsari A, Topham J, Karasinska J, Nelson J, Naso J, Jones SJM, Renouf D, Schaeffer DF, Bashashati A

The American journal of pathology2024Senior author

DOI: 10.1016/j.ajpath.2024.08.006 PMID: 39222907

Benchmarking bulk and single-cell variant-calling approaches on Chromium scRNA-seq and scATAC-seq libraries.

Wiens M, Farahani H, Scott RW, Underhill TM, Bashashati A

Genome research2024Senior author

DOI: 10.1101/gr.277066.122 PMID: 39147582

Multi-scale relational graph convolutional network for multiple instance learning in histopathology images.

Bazargani R, Fazli L, Gleave M, Goldenberg L, Bashashati A, Salcudean S

Medical image analysis2024

DOI: 10.1016/j.media.2024.103197 PMID: 38805765

Learning generalizable AI models for multi-center histopathology image classification.

Asadi-Aghbolaghi M, Darbandsari A, Zhang A, Contreras-Sanz A, Boschman J, Ahmadvand P, Köbel M, Farnell D, Huntsman DG, Churg A, Black PC, Wang G, Gilks CB, Farahani H, Bashashati A

NPJ precision oncology2024Senior author

DOI: 10.1038/s41698-024-00652-4 PMID: 39030380

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Ali Bashashati

Research

Latest papers

Latest funding

Regional tissue perfusion index (RTPI): a new optical-based metric for quantifying regional tissue perfusion.

Recommendations for Pathologist-Led Deployment of Artificial Intelligence Tools as Laboratory-Developed Tests.

Performance Assessment of a Deep Learning-based Algorithm for Ovarian Cancer Histotyping in an Independent Data Set.

A Deep Learning Framework for Classification of Neuroendocrine Neoplasm Whole Slide Images.

Benchmarking histopathology foundation models for ovarian cancer bevacizumab treatment response prediction from whole slide images.

ATEC23 Challenge: Automated prediction of treatment effectiveness in ovarian cancer using histopathological images.

A Deep Learning Approach for the Identification of the Molecular Subtypes of Pancreatic Ductal Adenocarcinoma Based on Whole Slide Pathology Images.

Benchmarking bulk and single-cell variant-calling approaches on Chromium scRNA-seq and scATAC-seq libraries.

Multi-scale relational graph convolutional network for multiple instance learning in histopathology images.

Learning generalizable AI models for multi-center histopathology image classification.

Next Generation Machine Learning for the Accurate Detection of DNA Variations from High-Throughput Sequencing Data

Frequent collaborators

Samuel Aparicio

Jiarui Ding

David Huntsman

Martin Hirst

Christian Steidl

Aline Talhouk

Basile Tessier-Cloutier

Adi Steif

Amy Jamieson

Angela Brooks-Wilson

Matthew Wiens

Septimiu Salcudean

Sohrab Shah

Shoukat Dedhar

Anne-Marie Mes-Masson

Robert Kridel

Larry Goldenberg

Babak Shadgan

Gang Wang

Youwen Zhou

Carl Hansen

Honglin Luo

Connie Eaves

Peter Watson

Nathalie Johnson

Michael Anglesio

Adam Shlien

Inanc Birol

Steven Jones

Sara Mostafavi