Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Domain-adapted Large Language Models for Classifying Nuclear Medicine Reports
26
Zitationen
5
Autoren
2023
Jahr
Abstract
Purpose: To evaluate the impact of domain adaptation on the performance of language models in predicting five-point Deauville scores on the basis of clinical fluorine 18 fluorodeoxyglucose PET/CT reports. Materials and Methods: testing. Results: ≤ .001). A physician given the task on a subset of the data had a five-class accuracy of 66%. Conclusion: . © RSNA, 2023See also the commentary by Abajian in this issue.
Ähnliche Arbeiten
TNM Classification of Malignant Tumours
1987 · 16.123 Zit.
A survey on deep learning in medical image analysis
2017 · 13.972 Zit.
Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening
2011 · 10.887 Zit.
The American Joint Committee on Cancer: the 7th Edition of the AJCC Cancer Staging Manual and the Future of TNM
2010 · 9.139 Zit.
UNet++: A Nested U-Net Architecture for Medical Image Segmentation
2018 · 8.772 Zit.