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Quantification of anomalies in rats’ spinal cords using autoencoders
M.E. Tschuchnig
*
, D. Zillner
, P. Romanelli
, D. Hercher
, P. Heimel
,
G.J. Oostingh
, S. Couillard-Després
,
M. Gadermayr
*
Corresponding author for this work
Health Sciences
Information Technologies and Digitalisation
Biomedical Sciences
Data Science & Analytics
University of Salzburg
Institute of Experimental Neuroregeneration, Spinal Cord Injury and Tissue Regeneration Center Salzburg
Austrian Cluster for Tissue Regeneration
Core Facility Hard Tissue and Biomaterial Research, Karl Donath Laboratory, University Clinic of Dentistry, Medical University of Vienna
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Keyphrases
Rat Spinal Cord
100%
Autoencoder
100%
Lesion Quantification
100%
Spinal Cord Lesion
66%
Spine
66%
Computed Tomography
33%
Further Application
33%
Variational Autoencoder
33%
Medical Diagnostics
33%
Anomaly-based
33%
Average Correlation
33%
Spinal Cord
33%
Quantification Method
33%
Weak Labels
33%
Micro-computed Tomography Scan
33%
Anomaly Detection System
33%
Area Feature
33%
Area-based
33%
Lesion Progression
33%
Cardiac Magnetic Resonance Imaging (cMRI)
33%
Immunology and Microbiology
Spinal Cord
100%
Rat
100%
Vertebral Column
40%
Computer Assisted Tomography
40%
Magnetic Resonance Imaging
20%
Micro-Computed Tomography
20%
Computer Science
Autoencoder
100%
Computer Assisted Tomography
66%
Vertebral Column
66%
Detection Algorithm
33%
Variational Autoencoder
33%
Biochemistry, Genetics and Molecular Biology
Rat
100%
Computer Assisted Tomography
100%
Magnetic Resonance Imaging
50%
Micro-Computed Tomography
50%
Material Science
Tomography
100%
Variational Autoencoder
50%
Magnetic Resonance Imaging
50%
Neuroscience
Spinal Cord
100%
Computed Tomography
20%
Vertebral Column
20%
Micro Computed Tomography
20%
Magnetic Resonance Imaging of Spine
20%
Anomaly Detection
20%