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A foundation model for generalized brain MRI analysis
Divyanshu Tak
, Biniam A Garomsa
, Tafadzwa L Chaunzwa
, Anna Zapaishchykova
, Juan Carlos Climent Pardo
, Zezhong Ye
, John Zielke
, Yashwanth Ravipati
, Sri Vajapeyam
, Maryam Mahootiha
, Ceilidh Smith
, Ariana M Familiar
, Kevin X Liu
, Sanjay Prabhu
, Pratiti Bandopadhayay
, Ali Nabavizadeh
, Sabine Mueller
,
Hugo Jwl Aerts
, Raymond Y Huang
, Tina Y Poussaint
Benjamin H Kann
*
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*
Corresponding author for this work
GROW - Basic and Translational Cancer Biology
Beeldvorming
DA BV Research
Carim - Imaging
Research output
:
Working paper / Preprint
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Preprint
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Keyphrases
Brain Magnetic Resonance Imaging
100%
Brain Imaging
100%
Foundation Models
100%
Magnetic Resonance Imaging Analysis
100%
Artificial Intelligence
66%
Adaptation
33%
Clinical Translation
33%
Patient Demographics
33%
Brain MRI
33%
Magnetic Resonance Imaging Data
33%
Disease Management
33%
Disease Diagnosis
33%
Biomarker Discovery
33%
Training Tasks
33%
Specific Model
33%
Low Quality Data
33%
Limited Training Data
33%
Imaging pipeline
33%
Downstream Applications
33%
Supervised Training
33%
Clinical Scenarios
33%
Self-supervised Learning
33%
Pipeline Framework
33%
Generalized Representation
33%
Multimodal Framework
33%
Application Adaptation
33%
INIS
nmr imaging
100%
brain
100%
foundations
100%
data
42%
applications
28%
artificial intelligence
28%
algorithms
14%
diseases
14%
patients
14%
spectra
14%
biological markers
14%
learning
14%
populations
14%
management
14%
pipelines
14%
Medicine and Dentistry
Magnetic Resonance Imaging
100%
Brain Imaging
100%
Biological Marker
33%
Patient Population
33%
Magnetic Resonance Imaging of Brain
33%
Neuroscience
Magnetic Resonance Imaging
100%
Brain Imaging
100%
Magnetic Resonance Imaging of Brain
33%
Computer Science
Artificial Intelligence
100%
Self-Supervised Learning
50%
Training Data
50%