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SST-DUNet: Smart Swin Transformer and Dense UNet for automated preclinical fMRI skull stripping
Sima Soltanpour
, Rachel Utama
, Arnold Chang
,
Md Taufiq Nasseef
, Dan Madularu
, Praveen Kulkarni
, Craig F. Ferris
, Chris Joslin
Mathematics
Carleton University
Northeastern University
Tessellis Ltd.
Research output
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peer-review
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Dive into the research topics of 'SST-DUNet: Smart Swin Transformer and Dense UNet for automated preclinical fMRI skull stripping'. Together they form a unique fingerprint.
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Neuroscience
Functional Magnetic Resonance Imaging
100%
Skull
100%
Magnetic Resonance Imaging
33%
Independent Component Analysis
16%
Brain Extraction
16%
Computer Science
Swin Transformer
100%
Deep Learning Method
12%
Class Imbalance
12%
Independent Component Analysis
12%
Preprocessing Step
12%
Self-Attention
12%
Dice Similarity Score
12%
Brain Structure
12%
Biochemistry, Genetics and Molecular Biology
Functional Magnetic Resonance Imaging
100%
Magnetic Resonance Imaging
33%
Engineering
Similarity
12%
Deep Learning Method
12%
State-of-the-Art Method
12%
Loss Function
12%
Independent Component Analysis
12%
Extractor
12%
Food Science
Magnetic Resonance Imaging
33%