End-to-end Training for Whole Image Breast Cancer Screening using An All Convolutional Design

An end-to-end training algorithm for the detection and classification of breast cancer on digital mammograms was created. In the initial training stage lesion annotations were used, but in subsequent stages, a whole image classifier was trained using only image-level labels, eliminating the reliance on rarely available lesion annotations. The simple all convolutional design provided superior performance in comparison with previous methods. For example, on the Digital Database for Screening Mammography (DDSM), the best single model achieved a per-image AUC of 0.88 on a holdout test set, and three-model averaging increased the AUC to 0.91. On an independent holdout set of images from the INbreast database, the best single model achieved a per-image AUC of 0.96. We also demonstrated that a whole image model trained on DDSM can be transferred to INbreast without using its lesion annotations and using only a small amount of INbreast data for fine-tuning. Code and model available at: https://github.com/lishen/end2end-all-conv