NuCLS: A scalable crowdsourcing, deep learning approach and dataset for nucleus classification, localization and segmentation

2 code implementations • 18 Feb 2021 • Mohamed Amgad, Lamees A. Atteya, Hagar Hussein, Kareem Hosny Mohammed, Ehab Hafiz, Maha A. T. 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 A. T. Elsebaie, Ahmed Ayad, Liza E. Hanna, Ahmed Gadallah, Mohamed Elkady, Bradley Drumheller, David Jaye, David Manthey, David A. Gutman, Habiba Elfandy, Lee A. D. Cooper

High-resolution mapping of cells and tissue structures provides a foundation for developing interpretable machine-learning models for computational pathology.

Benchmarking Interpretable Machine Learning +1