Author

Saman Sarraf

Baycrest HospitalORCID 0000-0003-3314-4281Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Assay:imaging· 3 Approach:deep learning· 2 Research Focus:differential expression· 2 Organism:human· 2 Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Approach:statistical model· 1 Research Focus:trajectory inference· 1

Frequent Co-Authors

Danielle D. DeSouzaiD· 2 shared Ghassem Tofighi· 2 shared John A. E. AndersoniD· 2 shared Alzheimer’s Disease Neuroimaging Initiative· 1 shared Arman Sarraf· 1 shared Milton Kabia· 1 shared The Alzheimer's Disease Neuroimagin ADNI· 1 shared

Activity by Year

2016-2023 · peak 1/yr

Co-Author Collaboration Network

7 top collaborators · 15 mutual co-author links

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2 shared papersNeuroscience

Danielle D. DeSouzaORCID

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3 papers

Recent·Most cited

NeuroscienceBrain SciencesPeer-reviewedFeb 3, 2023

OViTAD: Optimized Vision Transformer to Predict Various Stages of Alzheimer's Disease Using Resting-State fMRI and Structural MRI Data

Saman Sarraf, Arman Sarraf, Danielle D. DeSouza…·The Alzheimer's Disease Neuroimagin ADNI

This paper introduces OViTAD, an optimized vision transformer that predicts Alzheimer's disease stages using resting-state fMRI and structural MRI data, achieving high accuracy with fewer parameters.

84 citationstop 1%

imagingtransformer / foundation model

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BioinformaticsbioRxivPreprintJan 14, 2017

✦ In-depth digest

DeepAD: Alzheimer’s Disease Classification via Deep Convolutional Neural Networks using MRI and fMRI

Saman Sarraf, Danielle D. DeSouza, John A. E. Anderson…·Alzheimer’s Disease Neuroimaging Initiative

Caffe LeNet and GoogleNet trained on 2D slices from ADNI scans of adults over 75 report 98.84% to near-perfect accuracy, and 100% per subject for MRI after majority voting.

332 citations

humanimaging

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Bioinformatics2016 Future Technologies Conference (FTC)Peer-reviewedDec 1, 2016

Deep Learning-based Pipeline to Recognize Alzheimer′s Disease using fMRI Data

Saman Sarraf, Ghassem Tofighi

This paper uses a convolutional neural network to classify Alzheimer's disease fMRI data from healthy controls with 96.85% accuracy.

DOI 10.1109/ftc.2016.7821697

humanimaging

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