http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png

GAN-Based Image Enrichment in Digital Pathology Boosts Segmentation Accuracy

Gupta, Laxmi (Corresponding author)^RWTH* ; Klinkhammer, Barbara M. ; Boor, Peter^RWTH* ; Merhof, Dorit^RWTH* ; Gadermayr, Michael^RWTH*

In
Medical image computing and computer assisted intervention – MICCAI 2019 : 22nd International Conference, Shenzhen, China, October 13-17, 2019 : proceedings / Dinggang Shen, Tianming Liu, Terry M. Peters, Lawrence H. Staib, Caroline Essert, Sean Zhou, Pew-Thian Yap, Ali Khan (eds.). - Part 1, Seiten/Artikel-Nr: 631-639

2019

Konferenz/Event:Medical Image Computing and Computer Assisted Intervention : 22. International Conference , Shenzhen , Peoples R China , MICCAI 2019 , 2019-10-13 - 2019-10-17

ImpressumCham : Springer

Umfang631-639

ISBN978-3-030-32238-0, 978-3-030-32239-7

ReiheLecture notes in computer science ; 11764, Image Processing, Computer Vision, Pattern Recognition, and Graphics

Weitere Reihe: Springer eBooks

Online
DOI: 10.1007/978-3-030-32239-7_70
10.1007/978-3-030-32239-7_70

Einrichtungen

1. Lehr- und Forschungsgebiet Translationale Nephropathologie (528001-5 ; 925620 ; 925610)
2. Lehrstuhl für Bildverarbeitung (611710)

Dokumenttyp
Contribution to a book/Contribution to a conference proceedings

Format
online, print

Sprache
English

Anmerkung
Peer reviewed article

Externe Identnummern
SCOPUS: SCOPUS:2-s2.0-85075632285
WOS Core Collection: WOS:000548734200070

Interne Identnummern
RWTH-2023-11985
Datensatz-ID: 975328

Beteiligte Länder
Austria, Germany