  Automatic mammogram classification and mass segmentation play a critical role
in a computer-aided mammogram screening system. In this work, we present a
unified mammogram analysis framework for both whole-mammogram classification
and segmentation. Our model is designed based on a deep U-Net with residual
connections, and equipped with the novel hybrid deep supervision (HDS) scheme
for end-to-end multi-task learning. As an extension of deep supervision (DS),
HDS not only can force the model to learn more discriminative features like DS,
but also seamlessly integrates segmentation and classification tasks into one
model, thus the model can benefit from both pixel-wise and image-wise
supervisions. We extensively validate the proposed method on the widely-used
INbreast dataset. Ablation study corroborates that pixel-wise and image-wise
supervisions are mutually beneficial, evidencing the efficacy of HDS. The
results of 5-fold cross validation indicate that our unified model matches
state-of-the-art performance on both mammogram segmentation and classification
tasks, which achieves an average segmentation Dice similarity coefficient (DSC)
of 0.85 and a classification accuracy of 0.89. The code is available at
https://github.com/angrypudding/hybrid-ds.
