Z
Zhiyong Lu
Researcher at National Institutes of Health
Publications - 276
Citations - 20807
Zhiyong Lu is an academic researcher from National Institutes of Health. The author has contributed to research in topics: Chemistry & Deep learning. The author has an hindex of 57, co-authored 243 publications receiving 15086 citations. Previous affiliations of Zhiyong Lu include University of Colorado Boulder & University of Manchester.
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Proceedings ArticleDOI
ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases
TL;DR: The ChestX-ray dataset as discussed by the authors contains 108,948 frontal-view X-ray images of 32,717 unique patients with the text-mined eight disease image labels from the associated radiological reports using natural language processing.
Journal ArticleDOI
Opportunities and obstacles for deep learning in biology and medicine.
Travers Ching,Daniel Himmelstein,Brett K. Beaulieu-Jones,Alexandr A. Kalinin,Brian T. Do,Gregory P. Way,Enrico Ferrero,Paul-Michael Agapow,Michael Zietz,Michael M. Hoffman,Michael M. Hoffman,Wei Xie,Gail L. Rosen,Benjamin J. Lengerich,Johnny Israeli,Jack Lanchantin,Stephen Woloszynek,Anne E. Carpenter,Avanti Shrikumar,Jinbo Xu,Evan M. Cofer,Evan M. Cofer,Christopher A. Lavender,Srinivas C. Turaga,Amr Alexandari,Zhiyong Lu,David J. Harris,Dave DeCaprio,Yanjun Qi,Anshul Kundaje,Yifan Peng,Laura K. Wiley,Marwin H. S. Segler,Simina M. Boca,S. Joshua Swamidass,Austin Huang,Anthony Gitter,Anthony Gitter,Casey S. Greene +38 more
TL;DR: It is found that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art.
Proceedings ArticleDOI
ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases
TL;DR: A new chest X-rays database, namely ChestX-ray8, is presented, which comprises 108,948 frontal-view X-ray images of 32,717 unique patients with the text-mined eight disease image labels from the associated radiological reports using natural language processing, which is validated using the proposed dataset.
Journal ArticleDOI
BioCreative V CDR task corpus: a resource for chemical disease relation extraction
Jiao Li,Yueping Sun,Robin J. Johnson,Daniela Sciaky,Chih-Hsuan Wei,Robert Leaman,Allan Peter Davis,Carolyn J. Mattingly,Thomas C. Wiegers,Zhiyong Lu +9 more
TL;DR: The BC5CDR corpus was successfully used for the BioCreative V challenge tasks and should serve as a valuable resource for the text-mining research community.