nucleAIzer: A Parameter-free Deep Learning Framework for Nucleus Segmentation Using Image Style Transfer-Reference-Cited by-同舟云学术

nucleAIzer: A Parameter-free Deep Learning Framework for Nucleus Segmentation Using Image Style Transfer

Published:2020-05 Issue:5 Volume:10 Page:453-458.e6
ISSN:2405-4712
Container-title:Cell Systems
language:en
Short-container-title:Cell Systems

Author:

Hollandi Reka,Szkalisity Abel,Toth Timea,Tasnadi Ervin,Molnar Csaba,Mathe Botond,Grexa Istvan,Molnar Jozsef,Balind Arpad,Gorbe Mate,Kovacs Maria,Migh Ede,Goodman Allen,Balassa Tamas,Koos Krisztian,Wang Wenyu,Caicedo Juan Carlos,Bara Norbert,Kovacs Ferenc,Paavolainen Lassi,Danka Tivadar,Kriston Andras,Carpenter Anne Elizabeth,Smith Kevin,Horvath Peter

Funder

LENDULET-BIOMAG

European Regional Development Funds

Academy of Finland

National Institutes of Health

Swedish Research Council

Publisher

Elsevier BV

Subject

Cell Biology,Histology,Pathology and Forensic Medicine

Reference24 articles.

1. SegNet: a deep convolutional encoder-decoder architecture for image segmentation;Badrinarayanan;IEEE Trans. Pattern Anal. Mach. Intell.,2017