GANs for medical image analysis

Autor: Anirban Mukhopadhyay, Shadi Albarqouni, Christoph Baur, Bram van Ginneken, Nassir Navab, Arjan Kuijper, Salome Kazeminia
Přispěvatelé: Publica
Rok vydání: 2020
Předmět:
Lead Topic: Individual Health
FOS: Computer and information sciences
Computer Science - Machine Learning
Computer science
Computer Vision and Pattern Recognition (cs.CV)
Computer Science - Computer Vision and Pattern Recognition
medical imaging
Medicine (miscellaneous)
Machine Learning (stat.ML)
Surveys
Field (computer science)
Machine Learning (cs.LG)
Image (mathematics)
03 medical and health sciences
0302 clinical medicine
Statistics - Machine Learning
Artificial Intelligence
Image Processing
Computer-Assisted
Medical imaging
Computer Simulation
Segmentation
Interactive visualization
030304 developmental biology
Research Line: Computer vision (CV)
0303 health sciences
business.industry
Deep learning
deep learning
Data science
Labeled data
Neural Networks
Computer
Artificial intelligence
business
030217 neurology & neurosurgery
Generative grammar
Zdroj: Artificial Intelligence in Medicine. 109:101938
ISSN: 0933-3657
Popis: Generative Adversarial Networks (GANs) and their extensions have carved open many exciting ways to tackle well known and challenging medical image analysis problems such as medical image de-noising, reconstruction, segmentation, data simulation, detection or classification. Furthermore, their ability to synthesize images at unprecedented levels of realism also gives hope that the chronic scarcity of labeled data in the medical field can be resolved with the help of these generative models. In this review paper, a broad overview of recent literature on GANs for medical applications is given, the shortcomings and opportunities of the proposed methods are thoroughly discussed and potential future work is elaborated. We review the most relevant papers published until the submission date. For quick access, important details such as the underlying method, datasets and performance are tabulated. An interactive visualization which categorizes all papers to keep the review alive, is available at http://livingreview.in.tum.de/GANs_for_Medical_Applications.
Comment: Salome Kazeminia and Christoph Baur contributed equally to this work
Databáze: OpenAIRE
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