Accelerated training of bootstrap aggregation-based deep information extraction systems from cancer pathology reports

In machine learning, it is evident that the classification of the task performance increases if bootstrap aggregation (bagging) is applied. However, the bagging of deep neural networks takes tremendous amounts of computational resources and training time. The research question that we aimed to answe...

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Veröffentlicht in:	Journal of biomedical informatics 2020-10, Vol.110 (C), p.103564-103564, Article 103564
Hauptverfasser:	Yoon, Hong-Jun, Klasky, Hilda B., Gounley, John P., Alawad, Mohammed, Gao, Shang, Durbin, Eric B., Wu, Xiao-Cheng, Stroup, Antoinette, Doherty, Jennifer, Coyle, Linda, Penberthy, Lynne, Blair Christian, J., Tourassi, Georgia D.
Format:	Artikel
Sprache:	eng
Schlagworte:	Bootstrap aggregation Computing Methodologies Convolutional neural networks Data partitioning Deep learning Hierarchical self-attention networks High-performance computing Humans Information Storage and Retrieval Machine Learning Natural language processing Neoplasms Neural Networks, Computer
Online-Zugang:	Volltext
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