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

High-resolution mapping of cells and tissue structures provides a foundation for developing interpretable machine-learning models for computational pathology. Deep learning algorithms can provide accurate mappings given large numbers of labeled instances for training and validation. Generating adequ...

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Veröffentlicht in:	arXiv.org 2021-02
Hauptverfasser:	Mohamed, Amgad, Atteya, Lamees A, Hagar Hussein, Kareem Hosny Mohammed, Hafiz, Ehab, Elsebaie, Maha A T, Alhusseiny, Ahmed M, Mohamed Atef AlMoslemany, Elmatboly, Abdelmagid M, Pappalardo, Philip A, Rokia Adel Sakr, Mobadersany, Pooya, Ahmad, Rachid, Saad, Anas M, Alkashash, Ahmad M, Ruhban, Inas A, Alrefai, Anas, Elgazar, Nada M, Abdulkarim, Ali, Abo-Alela Farag, 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
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Annotations Classification Computer Science - Computer Vision and Pattern Recognition Computer Science - Learning Datasets Decision trees Deep learning Machine learning Mapping Nuclei (cytology) Pathology Quantitative Biology - Quantitative Methods Segmentation Training
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