Structured crowdsourcing enables convolutional segmentation of histology images

Amgad, Mohamed; Elfandy, Habiba; Hussein, Hagar; A Atteya, Lamees; Alaaeldin Temraz Elsebaie, Mai; S Abo Elnasr, Lamia; Sakr, Rokia; Salama Emad Salem, Hazem; F Ismail, Ahmed; M. Saad, Anas; Ahmed, Joumana; AT Elsebaie, Maha; Rahman, Mustafijur; A. Ruhban, Inas; M. Elgazar, Nada; Alagha, Yahya; Osman, Mohamed; M. Alhusseiny, Ahmed; M Khalaf, Mariam; F Younes, Abo-Alela; Abdulkarim, Ali; M Younes, Duaa; Gadallah, Ahmed; M Elkashash, Ahmad; Y Fala, Salma; M Zaki, Basma; Beezley, Jonathan; R. Chittajallu, Deepak; Manthey, David; A. Gutman, David; Cooper, Lee

doi:10.1093/bioinformatics/btz083

Public

Structured crowdsourcing enables convolutional segmentation of histology images

Published in Bioinformatics • Feb 5, 2019

Authors:

Mohamed Amgad

Habiba Elfandy

Hagar Hussein

Abstract

MOTIVATION: While deep-learning algorithms have demonstrated outstanding performance in semantic image segmentation tasks, large annotation datasets are needed to create accurate models. Annotation of histology images is challenging due to the effort and experience required to carefully delineate ti...

Subject

Computer science

Annotation

Segmentation

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