@inproceedings{06d01b85d199407dbb2b76000b802ba7,
title = "Classification and detection in mammograms with weak supervision via dual branch deep neural net",
abstract = "The high cost of generating expert annotations, poses a strong limitation for supervised machine learning methods in medical imaging. Weakly supervised methods may provide a solution to this tangle. In this study, we propose a novel deep learning architecture for multi-class classification of mammograms according to the severity of their containing anomalies, having only a global tag over the image. The suggested scheme further allows localization of the different types of findings in full resolution. The new scheme contains a dual branch network that combines region-level classification with region ranking. We evaluate our method on a large multi-center mammography dataset including 3,000 mammograms with various anomalies and demonstrate the advantages of the proposed method over a previous weakly-supervised strategy.",
keywords = "Deep learning, Mammography, Weakly supervision",
author = "Ran Bakalo and Rami Ben-Ari and Jacob Goldberger",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 16th IEEE International Symposium on Biomedical Imaging, ISBI 2019 ; Conference date: 08-04-2019 Through 11-04-2019",
year = "2019",
month = apr,
doi = "https://doi.org/10.1109/ISBI.2019.8759458",
language = "الإنجليزيّة",
series = "Proceedings - International Symposium on Biomedical Imaging",
publisher = "IEEE Computer Society",
pages = "1905--1909",
booktitle = "ISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging",
address = "الولايات المتّحدة",
}