Author

Parisa Mojiri Forooshani

University of TorontoORCID 0000-0003-4769-2895Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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

Frequent Co-Authors

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 Emmanuel E. Ntiri· 2 shared Fuqiang GaoiD· 2 shared Jane M. Lawrence‐DewariD· 2 shared

Activity by Year

2021-2022 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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2 shared papersNeuroscience

Alan R. MoodyORCID

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2 papers

Recent·Most cited

NeuroscienceHuman Brain MappingPeer-reviewedJan 28, 2022

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

Parisa Mojiri Forooshani, Mahdi Biparva, Emmanuel E. Ntiri…·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.

50 citationstop 5%

imagingBayesian inference

𝕏 PostBluesky

NeuroscienceNeuroinformaticsPeer-reviewedFeb 1, 2021

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…·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.

34 citations

imagingdeep learning

𝕏 PostBluesky