Domain-adapted Large Language Models for Classifying Nuclear Medicine Reports-Reference-Cited by-同舟云学术

Domain-adapted Large Language Models for Classifying Nuclear Medicine Reports

Published:2023-11-01 Issue:6 Volume:5 Page:
ISSN:2638-6100
Container-title:Radiology: Artificial Intelligence
language:en
Short-container-title:Radiology: Artificial Intelligence

Author:

Huemann Zachary¹^ORCID,Lee Changhee¹,Hu Junjie¹^ORCID,Cho Steve Y.¹,Bradshaw Tyler J.¹^ORCID

Affiliation:

1. From the Departments of Radiology (Z.H., C.L., S.Y.C., T.J.B.), Biostatistics (J.H.), and Computer Science (J.H.), University of Wisconsin–Madison, 1111 Highland Ave, Madison, WI 53705; and University of Wisconsin Carbone Cancer Center, Madison, Wis (S.Y.C.).

Funder

GE HealthCare

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.220281

Reference17 articles.

1. Performance of Multiple Pretrained BERT Models to Automate and Accelerate Data Annotation for Large Datasets

2. Deep Learning–based Assessment of Oncologic Outcomes from Natural Language Processing of Structured Radiology Reports

3. RadBERT: Adapting Transformer-based Language Models to Radiology

4. Deep Learning for Natural Language Processing in Radiology—Fundamentals and a Systematic Review

5. BioBART: Pretraining and Evaluation of A Biomedical Generative Language Model

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