Duplicate from helloerikaaa/cbis-ddsm-r

Co-authored-by: Erika <helloerikaaa@users.noreply.huggingface.co>

CITATION.cff

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+
+cff-version: 1.2.0
+message: "If you use this dataset, please cite the following paper."
+title: "CBIS-DDSM-R: A Curated Radiomic Feature Dataset for Breast Cancer Classification"
+type: dataset
+authors:
+  - family-names: Sánchez-Femat
+    given-names: Erika
+  - family-names: Galván-Tejada
+    given-names: Carlos E.
+  - family-names: Galván-Tejada
+    given-names: Jorge I.
+  - family-names: Gamboa-Rosales
+    given-names: Héctor
+  - family-names: Luna-García
+    given-names: Héctor
+  - family-names: Flores-Chaires
+    given-names: Luis A.
+  - family-names: Saldívar-Pérez
+    given-names: Javier
+  - family-names: Reveles-Martínez
+    given-names: Raúl
+  - family-names: Celaya-Padilla
+    given-names: José M.
+doi: 10.3390/data10110179
+url: https://doi.org/10.3390/data10110179
+journal: "Data"
+volume: 10
+issue: 11
+year: 2025
+pages: "179"

README.md

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+---
+license: gpl-3.0
+task_categories:
+- image-classification
+language:
+- en
+tags:
+- radiomics
+- bioniformatics
+- cancer
+- breast-cancer
+- medical
+pretty_name: CBIS-DDSM-R
+---
+
+# CBIS-DDSM-R: A Curated Radiomic Feature Dataset for Breast Cancer Classification
+
+## Dataset Summary
+CBIS-DDSM-R is an open-source, radiomics-ready extension of the Curated Breast Imaging Subset of the Digital Database for Screening Mammography (CBIS-DDSM). It is designed to facilitate reproducible radiomics and quantitative imaging research in breast cancer analysis.
+The dataset provides a standardized preprocessing pipeline for mammograms and includes IBSI-compliant radiomics features extracted using PyRadiomics. Clinical metadata and radiomics features are combined into a unified, machine-readable format, making CBIS-DDSM-R a robust benchmark for developing and validating radiomics-based breast cancer models.
+
+## Key Features
+- Standardized mammogram preprocessing pipeline
+- 93 radiomics features per lesion, extracted with PyRadiomics
+- Full compliance with Image Biomarker Standardisation Initiative (IBSI) guidelines
+- Unified dataset combining clinical metadata and radiomics features
+- Designed for reproducibility and benchmarking in breast cancer radiomics
+## Supported Tasks
+- Breast cancer characterization
+- Radiomics-based risk assessment
+- Feature selection and reproducibility studies
+- Benchmarking CAD and machine learning models
+## Dataset Structure
+The dataset is organized to support straightforward machine learning workflows:
+- Radiomics features stored in tabular format
+- Clinical and annotation metadata aligned at the lesion level
+- Clear identifiers linking features to mammographic views and cases
+## Intended Use
+CBIS-DDSM-R is intended for research and educational purposes, particularly for:
+- Radiomics and quantitative imaging studies
+- Development and validation of machine learning models
+- Reproducible research in medical imaging
+## Citation
+If you use this dataset, please cite the following paper.
+```bibtex
+
+@Article{data10110179,
+AUTHOR = {Sánchez-Femat, Erika and Galván-Tejada, Carlos E. and Galván-Tejada, Jorge I. and Gamboa-Rosales, Hamurabi and Luna-García, Huizilopoztli and Flores-Chaires, Luis Alberto and Saldívar-Pérez, Javier and Reveles-Martínez, Rafael and Celaya-Padilla, José M.},
+TITLE = {CBIS-DDSM-R: A Curated Radiomic Feature Dataset for Breast Cancer Classification},
+JOURNAL = {Data},
+VOLUME = {10},
+YEAR = {2025},
+NUMBER = {11},
+ARTICLE-NUMBER = {179},
+URL = {https://www.mdpi.com/2306-5729/10/11/179},
+ISSN = {2306-5729},
+ABSTRACT = {Early and accurate breast cancer detection is critical for patient outcomes. The Curated Breast Imaging Subset of the Digital Database for Screening Mammography (CBIS-DDSM) has been instrumental for computer-aided diagnosis (CAD) systems. However, the lack of a standardized preprocessing pipeline and consistent metadata has limited its utility for reproducible quantitative imaging or radiomics. This paper introduces CBIS-DDSM-R, an open-source, radiomics-ready extension of the original dataset. It provides an automated pipeline for preprocessing mammograms and extracts a standardized set of 93 radiomics features per lesion, adhering to Image Biomarker Standardisation Initiative (IBSI) guidelines using PyRadiomics. The resulting dataset combines clinical and radiomics data into a unified format, offering a robust benchmark for developing and validating reproducible radiomics models for breast cancer characterization.},
+DOI = {10.3390/data10110179}
+}
+```

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