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

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

doi:10.1117/12.2670228

6 March 2023 A named entity recognition framework using transformers to identify relevant clinical findings from mammographic radiological reports

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

Author Affiliations +

Proceedings Volume 12567, 18th International Symposium on Medical Information Processing and Analysis; 125670X (2023) https://doi.org/10.1117/12.2670228
Event: 18th International Symposium on Medical Information Processing and Analysis, 2022, Valparaíso, Chile

Abstract

Detecting and extracting findings in a radiological report is crucial for text mining tasks in several applications. In this case, a labeled process for the image associated with the radiological report in mammography and Spanish context for a computer vision model is required. This paper shows the methodology and process generated for this goal. This paper presents a Named Entity Recognition (NER) approach based on a transformer deep learning model, using a labeled corpus and fine-tuning process to find three concepts that compose a typical finding in a mammographic radiological report: laterality, location, and the finding. We add another concept in the labeled process, the negation, necessary to identify falses positive inside the text that writes the radiologist. Our model achieves an F1 score of 88.24% classifying the three principal concepts for a finding, product of the labeled and fine-tuning process. The results presented here will be used as input for future training work on a computer vision model.

Citation Download Citation

Eduardo Godoy, Steren Chabert, Marvin Querales, Julio Sotelo, Denis Parra, Carlos Fernandez, Diego Mellado, Alejandro Veloz, Scarlett Lever, Fabian Pardo, Ayleen Bertini, Yomar Molina, Claudia Díaz, Rodrigo Ferreira, and Rodrigo Salas "A named entity recognition framework using transformers to identify relevant clinical findings from mammographic radiological reports", Proc. SPIE 12567, 18th International Symposium on Medical Information Processing and Analysis, 125670X (6 March 2023); https://doi.org/10.1117/12.2670228

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
10 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Mammography

Data modeling

Transformers

Education and training

Visual process modeling

Performance modeling

Deep learning

Show All Keywords

Keywords/Phrases

Search In:

Publication Years