  Mammogram classification is directly related to computer-aided diagnosis of
breast cancer. Traditional methods requires great effort to annotate the
training data by costly manual labeling and specialized computational models to
detect these annotations during test. Inspired by the success of using deep
convolutional features for natural image analysis and multi-instance learning
for labeling a set of instances/patches, we propose end-to-end trained deep
multi-instance networks for mass classification based on whole mammogram
without the aforementioned costly need to annotate the training data. We
explore three different schemes to construct deep multi-instance networks for
whole mammogram classification. Experimental results on the INbreast dataset
demonstrate the robustness of proposed deep networks compared to previous work
using segmentation and detection annotations in the training.
