  Annotation cost is a bottleneck for collecting massive data in mammography,
especially for training deep neural networks. In this paper, we study the use
of heterogeneous levels of annotation granularity to improve predictive
performances. More precisely, we introduce a multi-task learning scheme for
training convolutional neural network (ConvNets), which combines segmentation
and classification, using image-level and pixel-level annotations. In this way,
different objectives can be used to regularize training by sharing intermediate
deep representations. Successful experiments are carried out on the Digital
Database of Screening Mammography (DDSM) to validate the relevance of the
proposed approach.
