@inproceedings{30c7a4bc00a449bdb3b7faf9901c139e,
title = "Breast density classification with deep convolutional neural networks",
abstract = "Breast density classification is an essential part of breast cancer screening. Although a lot of prior work considered this problem as a task for learning algorithms, to our knowledge, all of them used small and not clinically realistic data both for training and evaluation of their models. In this work, we explored the limits of this task with a data set coming from over 200,000 breast cancer screening exams. We used this data to train and evaluate a strong convolutional neural network classifier. In a reader study, we found that our model can perform this task comparably to a human expert.",
keywords = "Breast cancer screening, Breast density, Convolutional neural networks, Deep learning, Mammography",
author = "Nan Wu and Geras, {Krzysztof J.} and Yiqiu Shen and Jingyi Su and Kim, {S. Gene} and Eric Kim and Stacey Wolfson and Linda Moy and Kyunghyun Cho",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 ; Conference date: 15-04-2018 Through 20-04-2018",
year = "2018",
month = sep,
day = "10",
doi = "10.1109/ICASSP.2018.8462671",
language = "English (US)",
isbn = "9781538646588",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "6682--6686",
booktitle = "2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings",
}