作者
Mohamed Amgad, Lamees A Atteya, Hagar Hussein, Kareem Hosny Mohammed, Ehab Hafiz, Maha AT Elsebaie, Ahmed M Alhusseiny, Mohamed Atef AlMoslemany, Abdelmagid M Elmatboly, Philip A Pappalardo, Rokia Adel Sakr, Pooya Mobadersany, Ahmad Rachid, Anas M Saad, Ahmad M Alkashash, Inas A Ruhban, Anas Alrefai, Nada M Elgazar, Ali Abdulkarim, Abo-Alela Farag, Amira Etman, Ahmed G Elsaeed, Yahya Alagha, Yomna A Amer, Ahmed M Raslan, Menatalla K Nadim, Mai AT Elsebaie, Ahmed Ayad, Liza E Hanna, Ahmed Gadallah, Mohamed Elkady, Bradley Drumheller, David Jaye, David Manthey, David A Gutman, Habiba Elfandy, Lee AD Cooper
发表日期
2022
期刊
GigaScience
卷号
11
页码范围
giac037
出版商
Oxford University Press
简介
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 effort required from pathologists.
Results
This article describes a novel collaborative framework for engaging crowds of medical students and pathologists to produce quality labels for cell nuclei. We used this approach to produce the NuCLS dataset, containing >220,000 annotations of cell nuclei in breast cancers. This builds on prior work labeling tissue regions to produce an integrated tissue region- and cell-level annotation dataset for training that is the largest such resource for multi-scale analysis of breast cancer histology. This article presents data and analysis results for single …
学术搜索中的文章
M Amgad, LA Atteya, H Hussein, KH Mohammed… - GigaScience, 2022