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Experiences on generating synthetic medical data with GAN models
Harri Pölönen
, Niki Loppi
, Christian Hundt
NVIDIA Helsinki Oy
Research output
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Contribution to conference
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Conference Poster
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Scientific
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peer-review
101
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Keyphrases
Synthetic MRI
100%
GAN Model
100%
Synthetic Medical Data
100%
Mode Collapse
75%
Progressive GAN
50%
Medical Experts
50%
Medical Data
25%
Privacy Protection
25%
Medical Imaging
25%
Data Sharing
25%
Publicly Available
25%
CT Volume
25%
Medical Conditions
25%
Quality Issues
25%
Magnetic Resonance
25%
Low Variation
25%
Small Dataset
25%
State-of-the-art Techniques
25%
Brain MRI
25%
MRI Data
25%
Targets of Interest
25%
Imaging Modalities
25%
National Legislation
25%
Large Memory
25%
Generated Image
25%
Memory Footprint
25%
Facial Image
25%
Data Collapse
25%
Privacy Sensitive
25%
CT Segmentation
25%
MRI Quality
25%
Original Mode
25%
3D Medical Imaging
25%
Healthy Human Subjects
25%
NVIDIA
25%
Open-source Toolkit
25%
Computer Science
Updated Version
100%
Data Sharing
100%
Research Organization
100%
Medical Imaging
100%
Open Source
100%
General Data Protection Regulation
100%
facial image
100%
Memory Footprint
100%
Medical Condition
100%
Imaging Modality
100%
Artificial Intelligence
100%
INIS
data
100%
nmr imaging
100%
gallium nitrides
100%
algorithms
83%
images
50%
volume
33%
variations
33%
humans
33%
datasets
33%
performance
16%
inspection
16%
legislation
16%
magnetic resonance
16%
brain
16%
computerized tomography
16%
hospitals
16%
biomedical radiography
16%
augmentation
16%
Engineering
Medical Data
100%
Larger Quantity
33%
Good Result
33%
Quality Issue
33%
State-of-the-Art Method
33%
Generated Image
33%
Imaging Modality
33%
Memory Footprint
33%
Medical Condition
33%
Brain Image
33%
Artificial Intelligence
33%
Earth and Planetary Sciences
State of the Art
100%
Artificial Intelligence
100%
Biochemistry, Genetics and Molecular Biology
Magnetic Resonance Imaging
100%
Normal Human
16%
Cone Beam Computed Tomography
16%
Artificial Intelligence
16%
Homo sapiens
16%
Neuroscience
Magnetic Resonance Imaging
100%