A named entity recognition framework using transformers to identify relevant clinical findings from mammographic radiological reports-Reference-Cited by-同舟云学术

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A named entity recognition framework using transformers to identify relevant clinical findings from mammographic radiological reports

Published:2023-03-06 Issue: Volume: Page:
ISSN:
Container-title:18th International Symposium on Medical Information Processing and Analysis
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Author:

Godoy Eduardo,Chabert Steren,Querales Marvin,Sotelo Julio,Parra Denis,Fernández Carlos,Mellado Diego,Veloz Alejandro,Lever Scarlett,Pardo Favian,Bertini Ayleen,Molina Yomar,Díaz Claudia,Ferreira Rodrigo,Salas Rodrigo

Publisher

SPIE

Reference19 articles.

1. Radiology Reporting and Communications

2. Natural Language Processing in Radiology: A Systematic Review

3. Extracting clinical terms from radiology reports with deep learning

4. Automatic information extraction from unstructured mammography reports using distributed semantics

5. Automatic inference of BI-RADS final assessment categories from narrative mammography report findings

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1. Torwards Trustworthy Machine Learning based systems: Evaluating breast cancer predictions interpretability using Human Centered Machine Learning and UX Techniques;Communications in Computer and Information Science;2023

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