@inproceedings{6602b4727516423c8bb4f782ab2dba5a,
title = "Localization, segmentation, and classification of mammographic abnormalities using deep learning",
abstract = "Breast cancer is a disease caused by abnormal growth of cells in the breast. We have investigated a deep learning pipeline, which provides classification (e.g. normal/ abnormal), and subsequently localization and segmentation of abnormalities. We have used the digital database for screening mammography in this work. The contributions of this paper are two-fold. First, we classify between normal and abnormal mammograms with a 100% training and 98.34% testing accuracy. Second, a framework is proposed to localize and segment abnormalities from abnormal images with a training loss of 0.57 and a testing loss of 0.55 where the multi-task loss function combines the loss of classification, localization, and segmentation mask.",
keywords = "classification, deep learning, localization, mammographic abnormalities, masked region-based, segmentation",
author = "Adeela Islam and Zobia Suhail and Reyer Zwiggelaar",
note = "Publisher Copyright: {\textcopyright} 2024 SPIE.; 17th International Workshop on Breast Imaging, IWBI 2024 ; Conference date: 09-06-2024 Through 12-06-2024",
year = "2024",
doi = "10.1117/12.3026998",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Giger, {Maryellen L.} and Whitney, {Heather M.} and Karen Drukker and Hui Li",
booktitle = "17th International Workshop on Breast Imaging, IWBI 2024",
address = "United States of America",
}
