Author

Tony Xu

University of TorontoORCID 0000-0002-9825-1005Unclaimed ORCID profile · follow to get notified of new preprints

Additional preprints under this name without ORCID

Tony Xu2 papers · Australian National University

Biology Disciplines

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Neuroscience· 3

Signature Assays, Organisms & Methods

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Assay:imaging· 3 Research Focus:cell type annotation· 2 Approach:deep learning· 2 Research Focus:dimensionality reduction· 2 Assay:spatial transcriptomics· 2 Research Focus:clustering· 1 Approach:contrastive learning· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Bojana StefanoviciD· 3 shared Maged GoubraniD· 3 shared Adrienne E. DorriD· 2 shared Ahmadreza AttarpouriD· 2 shared Isabelle AubertiD· 2 shared JoAnne McLauriniD· 2 shared Jonas Osmann· 2 shared Matthew W RozakiD· 2 shared

Activity by Year

2025-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 43 mutual co-author links

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3 shared papersNeuroscience

Bojana StefanovicORCID

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3 papers

Recent·Most cited

NeurosciencebioRxivPreprintOct 22, 2026

Aberrant cortical reorganization and astrogliosis underlie memory deficit post-traumatic brain injury: a longitudinal multiscale multimodal study

Jazlynn Tan, Shruti Patel, Tony Xu…·Maged Goubran

This study uses multiscale multimodal data integration in a mouse model of traumatic brain injury to identify biomarkers of memory deficits and network dedifferentiation.

DOI 10.64898/2026.10.16.759571

mousespatial transcriptomics

NeurosciencebioRxivPreprintNov 14, 2025

Quantitative profiling of whole-brain projectomes at single-axon resolution using deep learning and high-resolution light sheet microscopy

Ahmadreza Attarpour, Misha Raffiee, Tony Xu…·Maged Goubran

MAPL3 enables whole-brain projectome mapping at single-axon resolution using deep learning and light sheet microscopy, outperforming existing methods across diverse datasets.

1 citation

imagingspatial transcriptomics

NeurosciencebioRxivPreprintJan 20, 2025

A self-supervised deep learning pipeline for segmentation in two-photon fluorescence microscopy

Emmanuel E. Ntiri, Tony Xu, Matthew W Rozak…·Maged Goubran

This paper develops a self-supervised deep learning pipeline for TPFM image segmentation, showing that SSL models can match or exceed fully supervised models with less labeled data.

2 citations

imagingdeep learning