Y
Yifan Peng
Researcher at Cornell University
Publications - 95
Citations - 9273
Yifan Peng is an academic researcher from Cornell University. The author has contributed to research in topics: Deep learning & Computer science. The author has an hindex of 26, co-authored 90 publications receiving 5931 citations. Previous affiliations of Yifan Peng include University UCINF & University of Manchester.
Papers
More filters
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.
Proceedings ArticleDOI
Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets.
TL;DR: The Biomedical Language Understanding Evaluation (BLUE) benchmark is introduced to facilitate research in the development of pre-training language representations in the biomedicine domain and it is found that the BERT model pre-trained on PubMed abstracts and MIMIC-III clinical notes achieves the best results.