Image super-resolution using progressive generative adversarial networks for medical image analysis

Computerized Medical Imaging and Graphics

Volumn 71, Issue , 2019, Pages 30-39

  Mahapatra, Dwarikanath ^a   Bozorgtabar, Behzad ^b   Garnavi, Rahil ^a  

^a IBM RESEARCH   (United States)

^b EPF Laussane   (Switzerland)

Author keywords

Adversarial networks; Image super resolution; MRI; Pathology; Progressive generative models; Retinal fundus

Indexed keywords

IMAGE ANALYSIS; IMAGE QUALITY; IMAGE SEGMENTATION; MAGNETIC RESONANCE; MAGNETIC RESONANCE IMAGING; MEDICAL IMAGING; OPHTHALMOLOGY; OPTICAL RESOLVING POWER; PATHOLOGY;

ACQUISITION PARAMETERS; ADVERSARIAL NETWORKS; GENERATIVE MODEL; IMAGE QUALITY IMPROVEMENTS; IMAGE SUPER RESOLUTIONS; LOW RESOLUTION IMAGES; MAGNETIC RESONANCE SCANNERS; RETINAL FUNDUS;

IMAGE ENHANCEMENT;

ARTICLE; EYE FUNDUS; IMAGE ANALYSIS; IMAGE QUALITY; LOSS OF FUNCTION MUTATION; NUCLEAR MAGNETIC RESONANCE IMAGING; ALGORITHM; ANATOMIC LANDMARK; DIAGNOSTIC IMAGING; HEART VENTRICLE; HUMAN; IMAGE ENHANCEMENT; IMAGE PROCESSING; PROCEDURES; RETINA BLOOD VESSEL;

ALGORITHMS; ANATOMIC LANDMARKS; FUNDUS OCULI; HEART VENTRICLES; HUMANS; IMAGE ENHANCEMENT; IMAGE PROCESSING, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; RETINAL VESSELS;

EID: 85056921498     PISSN: 08956111     EISSN: 18790771     Source Type: Journal    
DOI: 10.1016/j.compmedimag.2018.10.005     Document Type: Article

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