TECRR: a benchmark dataset of radiological reports for BI-RADS classification with machine learning, deep learning, and large language model baselines

TECRR: a benchmark dataset of radiological reports for BI-RADS classification with machine learning, deep learning, and large language model baselines

Abstract Background Recently, machine learning (ML), deep learning (DL), and natural language processing (NLP) have provided promising results in the free-form radiological reports’ classification in the respective medical domain. In order to classify radiological reports properly, a high-quality an...

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Bibliographic Details
Main Authors: Sadam Hussain, Usman Naseem, Mansoor Ali, Daly Betzabeth Avendaño Avalos, Servando Cardona-Huerta, Beatriz Alejandra Bosques Palomo, Jose Gerardo Tamez-Peña
Format: Article
Language:English
Published: BMC 2024-10-01
Series:BMC Medical Informatics and Decision Making
Subjects:
BI-RADS classification
Breast radiological reports
TF-IDF
Word2vec
NLP
ML
Online Access:https://doi.org/10.1186/s12911-024-02717-7

Internet

https://doi.org/10.1186/s12911-024-02717-7

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