Zobrazeno 1 - 3
of 3
pro vyhledávání: '"Jesse C. Rayan"'
Publikováno v:
Radiol Artif Intell
PURPOSE: To develop and evaluate domain-specific and pretrained bidirectional encoder representations from transformers (BERT) models in a transfer learning task on varying training dataset sizes to annotate a larger overall dataset. MATERIALS AND ME
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f1c1f701c927bce262b5758211e9b99f
https://europepmc.org/articles/PMC9344209/
https://europepmc.org/articles/PMC9344209/
Autor:
Irene Dixe de Oliveira Santo, Aaron Maybury, Di Wu, Ilan Y. Benador, Pedro V. Staziaki, Venkatesh Saligrama, Feng Nan, Neha Gangasani, Stephan W. Anderson, Jesse C. Rayan, Jonathan Scalera
Publikováno v:
European radiology. 31(7)
To develop machine learning (ML) models capable of predicting ICU admission and extended length of stay (LOS) after torso (chest, abdomen, or pelvis) trauma, by using clinical and/or imaging data. This was a retrospective study of 840 adult patients
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2d282268a3ddaf1f0f5c685ba99cd5e9
https://pubmed.ncbi.nlm.nih.gov/33475772
https://pubmed.ncbi.nlm.nih.gov/33475772
Publikováno v:
Radiol Artif Intell
PURPOSE: To determine the feasibility of using deep learning with a multiview approach, similar to how a human radiologist reviews multiple images, for binomial classification of acute pediatric elbow radiographic abnormalities. MATERIALS AND METHODS
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d275b6a32427d10294beb48a6f643f22
https://pubmed.ncbi.nlm.nih.gov/33937781
https://pubmed.ncbi.nlm.nih.gov/33937781