GANs for medical image analysis
Copyright © 2020 Elsevier B.V. All rights reserved..
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/.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2020 |
|---|---|
| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:109 |
|---|---|
| Enthalten in: |
Artificial intelligence in medicine - 109(2020) vom: 13. Sept., Seite 101938 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Kazeminia, Salome [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Deep learning |
|---|
| Anmerkungen: |
Date Completed 15.11.2021 Date Revised 15.11.2021 published: Print-Electronic Citation Status MEDLINE |
|---|
| doi: |
10.1016/j.artmed.2020.101938 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM332941779 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM332941779 | ||
| 003 | DE-627 | ||
| 005 | 20231225220618.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231225s2020 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.artmed.2020.101938 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1109.xml |
| 035 | |a (DE-627)NLM332941779 | ||
| 035 | |a (NLM)34756215 | ||
| 035 | |a (PII)S0933-3657(19)31151-0 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Kazeminia, Salome |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a GANs for medical image analysis |
| 264 | 1 | |c 2020 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 15.11.2021 | ||
| 500 | |a Date Revised 15.11.2021 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2020 Elsevier B.V. All rights reserved. | ||
| 520 | |a 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/ | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a Review | |
| 650 | 4 | |a Deep learning | |
| 650 | 4 | |a Generative adversarial networks | |
| 650 | 4 | |a Medical imaging | |
| 650 | 4 | |a Survey | |
| 700 | 1 | |a Baur, Christoph |e verfasserin |4 aut | |
| 700 | 1 | |a Kuijper, Arjan |e verfasserin |4 aut | |
| 700 | 1 | |a van Ginneken, Bram |e verfasserin |4 aut | |
| 700 | 1 | |a Navab, Nassir |e verfasserin |4 aut | |
| 700 | 1 | |a Albarqouni, Shadi |e verfasserin |4 aut | |
| 700 | 1 | |a Mukhopadhyay, Anirban |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Artificial intelligence in medicine |d 1995 |g 109(2020) vom: 13. Sept., Seite 101938 |w (DE-627)NLM075578344 |x 1873-2860 |7 nnns |
| 773 | 1 | 8 | |g volume:109 |g year:2020 |g day:13 |g month:09 |g pages:101938 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1016/j.artmed.2020.101938 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 109 |j 2020 |b 13 |c 09 |h 101938 | ||