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arXiv:1707.06978 [cs.CV]AbstractReferencesReviewsResources

A Multi-Scale CNN and Curriculum Learning Strategy for Mammogram Classification

William Lotter, Greg Sorensen, David Cox

Published 2017-07-21Version 1

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.

Comments: Accepted to MICCAI 2017 Workshop on Deep Learning in Medical Image Analysis
Categories: cs.CV
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