Kazeminia, Salome ; Baur, Christoph ; Kuijper, Arjan ; Van Ginneken, Bram ; Navab, Nassir ; Albarqouni, Shadi ; Mukhopadhyay, Anirban (2020)
GANs for medical image analysis.
In: Artificial Intelligence in Medicine, 109
doi: 10.1016/j.artmed.2020.101938
Artikel, Bibliographie
Kurzbeschreibung (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/.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2020 |
| Autor(en): | Kazeminia, Salome ; Baur, Christoph ; Kuijper, Arjan ; Van Ginneken, Bram ; Navab, Nassir ; Albarqouni, Shadi ; Mukhopadhyay, Anirban |
| Art des Eintrags: | Bibliographie |
| Titel: | GANs for medical image analysis |
| Sprache: | Englisch |
| Publikationsjahr: | September 2020 |
| Verlag: | Elsevier ScienceDirect |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Artificial Intelligence in Medicine |
| Jahrgang/Volume einer Zeitschrift: | 109 |
| DOI: | 10.1016/j.artmed.2020.101938 |
| Kurzbeschreibung (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/. |
| Freie Schlagworte: | Deep learning, Medical imaging, Surveys |
| Zusätzliche Informationen: | Art.No.: 101938 |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme 20 Fachbereich Informatik > Mathematisches und angewandtes Visual Computing |
| Hinterlegungsdatum: | 16 Dez 2020 08:06 |
| Letzte Änderung: | 02 Mär 2022 13:55 |
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