A Multi-Scale CNN and Curriculum Learning Strategy for Mammogram Classification
This work addresses the challenge of early breast cancer detection via mammography screening, which affects millions of patients annually, representing a domain-specific incremental improvement.
The paper tackles the problem of classifying mammograms for breast cancer detection by introducing a multi-scale CNN with curriculum learning, achieving 0.92 AUROC on the DDSM dataset.
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.