RadBERT: Adapting Transformer-based Language Models to Radiology-Reference-Cited by-同舟云学术

RadBERT: Adapting Transformer-based Language Models to Radiology

Published:2022-07-01 Issue:4 Volume:4 Page:
ISSN:2638-6100
Container-title:Radiology: Artificial Intelligence
language:en
Short-container-title:Radiology: Artificial Intelligence

Author:

Yan An¹,McAuley Julian¹,Lu Xing¹^ORCID,Du Jiang¹,Chang Eric Y.¹^ORCID,Gentili Amilcare¹,Hsu Chun-Nan¹^ORCID

Affiliation:

1. From the University of California, San Diego, 9500 Gilman Dr, La Jolla, CA 92093-0608 (A.Y., J.M., X.L., J.D., E.Y.C., A.G., C.N.H.); and Veterans Affairs San Diego Healthcare System, San Diego, Calif (E.Y.C., A.G.).

Funder

Department of Defense

National Science Foundation

Publisher

Radiological Society of North America (RSNA)

Subject

Artificial Intelligence,Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology

Link

http://pubs.rsna.org/doi/pdf/10.1148/ryai.210258

Reference11 articles.

1. BioBERT: a pre-trained biomedical language representation model for biomedical text mining

2. MIMIC-III, a freely accessible critical care database

3. Identifying and characterizing highly similar notes in big clinical note datasets

4. Preparing a collection of radiology examinations for distribution and retrieval

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