Your browser doesn't support javascript.
loading
Impact of multi-source data augmentation on performance of convolutional neural networks for abnormality classification in mammography.
Hwang, InChan; Trivedi, Hari; Brown-Mulry, Beatrice; Zhang, Linglin; Nalla, Vineela; Gastounioti, Aimilia; Gichoya, Judy; Seyyed-Kalantari, Laleh; Banerjee, Imon; Woo, MinJae.
Affiliation
  • Hwang I; School of Data Science and Analytics, Kennesaw State University, Kennesaw, GA, United States.
  • Trivedi H; Department of Radiology, Emory University, Atlanta, GA, United States.
  • Brown-Mulry B; School of Data Science and Analytics, Kennesaw State University, Kennesaw, GA, United States.
  • Zhang L; School of Data Science and Analytics, Kennesaw State University, Kennesaw, GA, United States.
  • Nalla V; Department of Information Technology, Kennesaw State University, Kennesaw, GA, United States.
  • Gastounioti A; Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, United States.
  • Gichoya J; Department of Radiology, Emory University, Atlanta, GA, United States.
  • Seyyed-Kalantari L; Department of Electrical Engineering and Computer Science, York University, Toronto, ON, Canada.
  • Banerjee I; Department of Radiology, Mayo Clinic Arizona, Phoenix, AZ, United States.
  • Woo M; School of Data Science and Analytics, Kennesaw State University, Kennesaw, GA, United States.
Front Radiol ; 3: 1181190, 2023.
Article in En | MEDLINE | ID: mdl-37588666
Key words
Fulltext
Add to My VHL
Print
XML
PubMed Links
Search on Google

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Front Radiol Year: 2023 Document type: Article Affiliation country: United States Country of publication: Switzerland
Fulltext
Add to My VHL
Print
XML
PubMed Links
Search on Google

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Front Radiol Year: 2023 Document type: Article Affiliation country: United States Country of publication: Switzerland