@inproceedings{1f524366fd774721b6c0c3fd2d05b2cd,
title = "Mammographic mass identification in dense breasts using multi-scale analysis of structured micro-patterns",
abstract = "The paper proposes a novel approach for the identification of cancerous regions located in a dense part of a breast. This task is particularly challenging even for experienced radiologists due to lack of clear boundaries between the cancerous and normal tissue. Multi-scale analysis of structured micro-patterns generated from local binary patterns (LBP) was used to generate a very small number of features which allowed for successful detection of cancerous regions. The proposed technique was tested on two publicly available datasets: Digital Database for Screening Mammography (DDSM) and INbreast. The area under the receiver operating characteristic (AUC) curve for DDSM with 2 features only was 0.99 and 0.92 for INbreast with 3 features.",
keywords = "Breast cancer, CAD, Dense ROI, Local binary pattern, Machine learning, Mammography, Structured micro-patterns",
author = "Shelda Sajeev and Mariusz Bajger and Gobert Lee and Chisako Muramatsu and Hiroshi Fujita",
year = "2020",
month = jan,
day = "1",
doi = "10.1117/12.2564272",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Hilde Bosmans and Nicholas Marshall and {Van Ongeval}, Chantal",
booktitle = "15th International Workshop on Breast Imaging, IWBI 2020",
note = "15th International Workshop on Breast Imaging, IWBI 2020 ; Conference date: 25-05-2020 Through 27-05-2020",
}