Screening mammography is an important front-line tool for the early detection of breast cancer, and some 39 million exams are conducted each year in the United States alone. Here, we describe a multi-scale convolutional neural network (CNN) trained with a curriculum learning strategy that achieves high levels of accuracy in classifying mammograms. Specifically, we first train CNN-based patch classifiers on segmentation masks of lesions in mammograms, and then use the learned features to initialize a scanning-based model that renders a decision on the whole image, trained end-to-end on outcome data. We demonstrate that our approach effectively handles the "needle in a haystack" nature of full-image mammogram classification, achieving 0.92 AUROC on the DDSM dataset.
@article{arxiv.1707.06978,
title = {A Multi-Scale CNN and Curriculum Learning Strategy for Mammogram Classification},
author = {William Lotter and Greg Sorensen and David Cox},
journal= {arXiv preprint arXiv:1707.06978},
year = {2017}
}
Comments
Accepted to MICCAI 2017 Workshop on Deep Learning in Medical Image Analysis