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GANs for medical image analysis

Kazeminia, Salome and Baur, Christoph and Kuijper, Arjan and van Ginneken, Bram and Navab, Nassir and Albarqouni, Shadi and Mukhopadhyay, Anirban (2020):
GANs for medical image analysis.
In: Artificial Intelligence in Medicine, 109, p. 40. Elsevier ScienceDirect, ISSN 09333657,
DOI: 10.1016/j.artmed.2020.101938,
[Article]

Abstract

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, essential details such as the underlying method, datasets, and performance are tabulated. An interactive visualization that categorizes all papers to keep the review alive is available at http://livingreview.in.tum.de/GANs_for_Medical_Applications/.

Item Type: Article
Erschienen: 2020
Creators: Kazeminia, Salome and Baur, Christoph and Kuijper, Arjan and van Ginneken, Bram and Navab, Nassir and Albarqouni, Shadi and Mukhopadhyay, Anirban
Title: GANs for medical image analysis
Language: English
Abstract:

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, essential details such as the underlying method, datasets, and performance are tabulated. An interactive visualization that categorizes all papers to keep the review alive is available at http://livingreview.in.tum.de/GANs_for_Medical_Applications/.

Journal or Publication Title: Artificial Intelligence in Medicine
Journal volume: 109
Publisher: Elsevier ScienceDirect
Uncontrolled Keywords: Deep learning, Medical imaging, Surveys
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
20 Department of Computer Science > Mathematical and Applied Visual Computing
Date Deposited: 16 Dec 2020 08:06
DOI: 10.1016/j.artmed.2020.101938
Additional Information:

Art.No.: 101938

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