Structured crowdsourcing enables convolutional segmentation of histology images

• Published in: Bioinformatics Vol. 35; no. 18; pp. 3461 - 3467
• Main Authors: Amgad, Mohamed, Elfandy, Habiba, Hussein, Hagar, Atteya, Lamees A, Elsebaie, Mai A T, Abo Elnasr, Lamia S, Sakr, Rokia A, Salem, Hazem S E, Ismail, Ahmed F, Saad, Anas M, Ahmed, Joumana, Elsebaie, Maha A T, Rahman, Mustafijur, Ruhban, Inas A, Elgazar, Nada M, Alagha, Yahya, Osman, Mohamed H, Alhusseiny, Ahmed M, Khalaf, Mariam M, Younes, Abo-Alela F, Abdulkarim, Ali, Younes, Duaa M, Gadallah, Ahmed M, Elkashash, Ahmad M, Fala, Salma Y, Zaki, Basma M, Beezley, Jonathan, Chittajallu, Deepak R, Manthey, David, Gutman, David A, Cooper, Lee A D
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 15.09.2019
• Subjects: Original Papers

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 deli...