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Open framework for mammography-based breast cancer risk assessment

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Open framework for mammography-based breast cancer risk assessment. / Pertuz, Said; Torres, German F.; Tamimi, Rulla; Kämäräinen, Joni.

2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings. IEEE, 2019.

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Harvard

Pertuz, S, Torres, GF, Tamimi, R & Kämäräinen, J 2019, Open framework for mammography-based breast cancer risk assessment. in 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings. IEEE, IEEE EMBS International Conference on Biomedical and Health Informatics, Chicago, United States, 19/05/19. https://doi.org/10.1109/BHI.2019.8834599

APA

Pertuz, S., Torres, G. F., Tamimi, R., & Kämäräinen, J. (2019). Open framework for mammography-based breast cancer risk assessment. In 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings IEEE. https://doi.org/10.1109/BHI.2019.8834599

Vancouver

Pertuz S, Torres GF, Tamimi R, Kämäräinen J. Open framework for mammography-based breast cancer risk assessment. In 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings. IEEE. 2019 https://doi.org/10.1109/BHI.2019.8834599

Author

Pertuz, Said ; Torres, German F. ; Tamimi, Rulla ; Kämäräinen, Joni. / Open framework for mammography-based breast cancer risk assessment. 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings. IEEE, 2019.

Bibtex - Download

@inproceedings{d634e0f3ae524e8495bc99d62e7cbe8c,
title = "Open framework for mammography-based breast cancer risk assessment",
abstract = "In recent years, several studies have established a relationship between mammographic parenchymal patterns and breast cancer risk. However, there is a lack of publicly available data and software for objective comparison and clinical validation. This paper presents an open and adaptable implementation (OpenBreast v1.0) of a fully-Automatic computerized framework for mammographic image analysis for breast cancer risk assessment. OpenBreast implements mammographic image analysis in four stages: breast segmentation, detection of region-of-interests, feature extraction and risk scoring. For each stage, we provide implementations of several state-of-The-Art methods. The pipeline is tested on a set of 305 full-field digital mammography images corresponding to 84 patients (51 cases and 49 controls) from the breast cancer digital repository (BCDR). OpenBreast achieves a competitive AUC of 0.846 in breast cancer risk assessment. In addition, used jointly with widely accepted risk factors such as patient age and breast density, mammographic image analysis using OpenBreast shows a statistically significant improvement in performance with an AUC of 0.876 (\mathrm{p}<0.001). Our framework will be made publicly available and it is easy to incorporate new methods.",
keywords = "Breast cancer, Mammography, Parenchymal analysis, Risk assessment, Texture analysis",
author = "Said Pertuz and Torres, {German F.} and Rulla Tamimi and Joni K{\"a}m{\"a}r{\"a}inen",
note = "EXT={"}Pertuz, Said{"}",
year = "2019",
month = "5",
day = "1",
doi = "10.1109/BHI.2019.8834599",
language = "English",
booktitle = "2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings",
publisher = "IEEE",

}

RIS (suitable for import to EndNote) - Download

TY - GEN

T1 - Open framework for mammography-based breast cancer risk assessment

AU - Pertuz, Said

AU - Torres, German F.

AU - Tamimi, Rulla

AU - Kämäräinen, Joni

N1 - EXT="Pertuz, Said"

PY - 2019/5/1

Y1 - 2019/5/1

N2 - In recent years, several studies have established a relationship between mammographic parenchymal patterns and breast cancer risk. However, there is a lack of publicly available data and software for objective comparison and clinical validation. This paper presents an open and adaptable implementation (OpenBreast v1.0) of a fully-Automatic computerized framework for mammographic image analysis for breast cancer risk assessment. OpenBreast implements mammographic image analysis in four stages: breast segmentation, detection of region-of-interests, feature extraction and risk scoring. For each stage, we provide implementations of several state-of-The-Art methods. The pipeline is tested on a set of 305 full-field digital mammography images corresponding to 84 patients (51 cases and 49 controls) from the breast cancer digital repository (BCDR). OpenBreast achieves a competitive AUC of 0.846 in breast cancer risk assessment. In addition, used jointly with widely accepted risk factors such as patient age and breast density, mammographic image analysis using OpenBreast shows a statistically significant improvement in performance with an AUC of 0.876 (\mathrm{p}<0.001). Our framework will be made publicly available and it is easy to incorporate new methods.

AB - In recent years, several studies have established a relationship between mammographic parenchymal patterns and breast cancer risk. However, there is a lack of publicly available data and software for objective comparison and clinical validation. This paper presents an open and adaptable implementation (OpenBreast v1.0) of a fully-Automatic computerized framework for mammographic image analysis for breast cancer risk assessment. OpenBreast implements mammographic image analysis in four stages: breast segmentation, detection of region-of-interests, feature extraction and risk scoring. For each stage, we provide implementations of several state-of-The-Art methods. The pipeline is tested on a set of 305 full-field digital mammography images corresponding to 84 patients (51 cases and 49 controls) from the breast cancer digital repository (BCDR). OpenBreast achieves a competitive AUC of 0.846 in breast cancer risk assessment. In addition, used jointly with widely accepted risk factors such as patient age and breast density, mammographic image analysis using OpenBreast shows a statistically significant improvement in performance with an AUC of 0.876 (\mathrm{p}<0.001). Our framework will be made publicly available and it is easy to incorporate new methods.

KW - Breast cancer

KW - Mammography

KW - Parenchymal analysis

KW - Risk assessment

KW - Texture analysis

U2 - 10.1109/BHI.2019.8834599

DO - 10.1109/BHI.2019.8834599

M3 - Conference contribution

BT - 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings

PB - IEEE

ER -