Author

Emmanuel E. Ntiri

University of Toronto

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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Neuroscience· 3

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Approach:deep learning· 3 Research Focus:dimensionality reduction· 3 Assay:imaging· 3 Research Focus:benchmarking· 2 Approach:Bayesian inference· 1 Approach:contrastive learning· 1 Research Focus:software / tool· 1 Approach:statistical model· 1

Frequent Co-Authors

Maged GoubraniD· 3 shared Alan R. MoodyiD· 2 shared Anthony Edward LangiD· 2 shared Christopher J.M. ScottiD· 2 shared Dar D DowlatshahiiD· 2 shared Donna T. KwaniD· 2 shared Fuqiang GaoiD· 2 shared Jane M. Lawrence‐DewariD· 2 shared

Activity by Year

2021-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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3 shared papersNeuroscience

Maged GoubranORCID

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Author profile →

3 papers

Recent·Most cited

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

Emmanuel E. Ntiri, Tony Xu, Matthew W Rozak, Ahmadreza Attarpour, Adrienne E. Dorr, Bojana Stefanovic, 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.

bioRxiv · Jan 20, 2025 · PreprintNeuroscience

Deep Bayesian networks for uncertainty estimation and adversarial resistance of white matter hyperintensity segmentation

Parisa Mojiri Forooshani, Mahdi Biparva, Emmanuel E. Ntiri, Joel Ramirez, Lyndon Boone, Melissa F. Holmes, Sabrina M. Adamo, Fuqiang Gao, Miracle Ozzoude, Christopher J.M. Scott, Dar D Dowlatshahi, Jane M. Lawrence‐Dewar, Donna T. Kwan, Anthony Edward Lang, Karine Marcotte, , Maged Goubran

This paper introduces a Bayesian 3D U-Net for WMH segmentation that provides uncertainty estimates and is robust to imaging variations, achieving high accuracy and efficiency.

Human Brain Mapping · Jan 28, 2022Peer-reviewedNeuroscience

Improved segmentation of the intracranial and ventricular volumes in populations with cerebrovascular lesions and atrophy using 3D CNNs

Emmanuel E. Ntiri, Melissa F. Holmes, Parisa Mojiri Forooshani, Joel Ramirez, Fuqiang Gao, Miracle Ozzoude, Sabrina M. Adamo, Christopher J.M. Scott, Dar D Dowlatshahi, Jane M. Lawrence‐Dewar, Donna T. Kwan, Anthony Edward Lang, Sean P. Symons, Robert Bartha, Stephen C. Strother, , Maged Goubran

The paper introduces CNN-based models for segmenting intracranial and ventricular volumes in neuroimaging data, showing improved accuracy and efficiency compared to existing methods.

Neuroinformatics · Feb 1, 2021Peer-reviewedNeuroscience