Abstract
The assessment of breast density is crucial in the context of breast cancer screening, especially in populations with a higher percentage of dense breast tissues. This study introduces a novel data augmentation technique termed attention-guided erasing (AGE), devised to enhance the downstream classification of four distinct breast density categories in mammography following the BI-RADS recommendation in the Vietnamese cohort. The proposed method integrates supplementary information during transfer learning, utilizing visual attention maps derived from a vision transformer backbone trained using the self-supervised DINO method. These maps are utilized to erase background regions in the mammogram images, unveiling only the potential areas of dense breast tissues to the network. Through the incorporation of AGE during transfer learning with varying random probabilities, we consistently surpass classification performance compared to scenarios without AGE and the traditional random erasing transformation. We validate our methodology using the publicly available VinDr-Mammo dataset. Specifically, we attain a mean F1-score of 0.5910, outperforming values of 0.5594 and 0.5691 corresponding to scenarios without AGE and with random erasing (RE), respectively. This superiority is further substantiated by t-tests, revealing a p-value of p<0.0001, underscoring the statistical significance of our approach.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209–49.
Sickles EA, D’Orsi CJ, Bassett LWet al. ACR BI-RADS® Mammography. ACR BI-RADS® Atlas, Breast Imaging Reporting and Data System. Reston, VA, American College of Radiology, 2013:121–40.
Trieu PDY, Mello-Thoms C, Peat JK, Do TD, Brennan PC. Risk factors of female breast cancer in Vietnam: a case-control study. Cancer Res Treat. 2017;49(4):990–1000.
Brandt KR, Scott CG, Ma L, Mahmoudzadeh AP, Jensen MR, Whaley DH et al. Comparison of clinical and automated breast density measurements: implications for risk prediction and supplemental screening. Radiology. 2016;279(3):710–9.
Gardezi SJS, Elazab A, Lei B, Wang T. Breast cancer detection and diagnosis using mammographic data: systematic review. J Med Internet Res. 2019;21(7):e14464.
Nguyen HTX, Tran SB, Nguyen DB, Pham HH, Nguyen HQ. A novel multi-view deep learning approach for BI-RADS and density assessment of mammograms. IEEE EMBC. 2022:2144–8.
Maghsoudi OH, Gastounioti A, Scott C, Pantalone L,Wu FF, Cohen EA et al. Deep-LIBRA: an artificial-intelligence method for robust quantification of breast density with independent validation in breast cancer risk assessment. Med Image Anal. 2021;73:102138.
Caron M, Touvron H, Misra I, Jégou H, Mairal J, Bojanowski P et al. Emerging properties in self-supervised vision transformers. Proc ICCV. 2021.
Nguyen HT, Nguyen HQ, Pham HH, Lam K, Le LT, Dao M et al. VinDr-Mammo: a largescale benchmark dataset for computer-aided diagnosis in full-field digital mammography. Sci Data. 2023;10(1):277.
Panambur AB, Madhu P, Maier A. Effect of random histogram equalization on breast calcification analysis using deep learning. Proc BVM. Springer. 2022:173–8.
Zhong Z, Zheng L, Kang G, Li S, Yang Y. Proc AAAI. Vol. 34. (07). 2020:13001–8.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 Der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
About this paper
Cite this paper
Panambur, A.B., Yu, H., Bhat, S., Madhu, P., Bayer, S., Maier, A. (2024). Attention-guided Erasing. In: Maier, A., Deserno, T.M., Handels, H., Maier-Hein, K., Palm, C., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2024. BVM 2024. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-44037-4_8
Download citation
DOI: https://doi.org/10.1007/978-3-658-44037-4_8
Published:
Publisher Name: Springer Vieweg, Wiesbaden
Print ISBN: 978-3-658-44036-7
Online ISBN: 978-3-658-44037-4
eBook Packages: Computer Science and Engineering (German Language)