NuCLS: A scalable crowdsourcing approach and dataset for nucleus classification and segmentation in breast cancer

BACKGROUND: Deep learning enables accurate high-resolution mapping of cells and tissue structures that can serve as the foundation of interpretable machine-learning models for computational pathology. However, generating adequate labels for these structures is a critical barrier, given the time and...

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Detalles Bibliográficos
Autores principales: Amgad, Mohamed, Atteya, Lamees A, Hussein, Hagar, Mohammed, Kareem Hosny, Hafiz, Ehab, Elsebaie, Maha A T, Alhusseiny, Ahmed M, AlMoslemany, Mohamed Atef, Elmatboly, Abdelmagid M, Pappalardo, Philip A, Sakr, Rokia Adel, Mobadersany, Pooya, Rachid, Ahmad, Saad, Anas M, Alkashash, Ahmad M, Ruhban, Inas A, Alrefai, Anas, Elgazar, Nada M, Abdulkarim, Ali, Farag, Abo-Alela, Etman, Amira, Elsaeed, Ahmed G, Alagha, Yahya, Amer, Yomna A, Raslan, Ahmed M, Nadim, Menatalla K, Elsebaie, Mai A T, Ayad, Ahmed, Hanna, Liza E, Gadallah, Ahmed, Elkady, Mohamed, Drumheller, Bradley, Jaye, David, Manthey, David, Gutman, David A, Elfandy, Habiba, Cooper, Lee A D
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9112766/
https://www.ncbi.nlm.nih.gov/pubmed/35579553
http://dx.doi.org/10.1093/gigascience/giac037

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