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

In this work, we propose a curated and annotated benchmark dataset that can be used for BI-RADS and breast density category classification. We also provide baseline results of most ML, DL and LLMs models for BI-RADS classification that can be used as a starting point for future investigation. The main objective of this investigation is to provide a repository for the investigators who wish to enter the field to push the boundaries further.

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Publication Details

Journal
BMC Medical Informatics and Decision Making
Published
2024-10-24
DOI
https://doi.org/10.1186/s12911-024-02717-7
Citations
6
Primary Topic
AI in cancer detection
Type
article
Field-Weighted Citation Impact
2.08

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article

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

Servando Cardona‐Huerta, José G. Tamez‐Peña, Usman Naseem, Sadam Hussain et al.
6 citations
BMC Medical Informatics and Decision Making
AI in cancer detection
2.08
article

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

Servando Cardona‐Huerta, José G. Tamez‐Peña, Usman Naseem, Sadam Hussain, Beatriz Alejandra Bosques Palomo, Daly Betzabeth Avendaño Avalos, Mansoor Ali
article en
6 citations

Abstract

In this work, we propose a curated and annotated benchmark dataset that can be used for BI-RADS and breast density category classification. We also provide baseline results of most ML, DL and LLMs models for BI-RADS classification that can be used as a starting point for future investigation. The main objective of this investigation is to provide a repository for the investigators who wish to enter the field to push the boundaries further.

BMC Medical Informatics and Decision MakingVol. 24(1)
Tecnológico de Monterrey (MX), Macquarie University (AU)
Consejo Nacional de Ciencia y Tecnología, Instituto Tecnológico y de Estudios Superiores de Monterrey
Quality Education
Openalex Percentile: Top 11%
AI in cancer detection
2.08
DOIOpen Access PDFOpenAlex
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TECRR: a benchmark dataset of radiological reports for BI-RADS classification with machine learning, deep learning, and large language model baselines — Servando Cardona‐Huerta, José G. Tamez‐Peña, et al. · BMC Medical Informatics and Decision Making (2024) | TGRS Research Map | TGRS