  The deep learning technique has been shown to be effectively addressed
several image analysis tasks in the computer-aided diagnosis scheme for
mammography. The training of an efficacious deep learning model requires large
data with diverse styles and qualities. The diversity of data often comes from
the use of various scanners of vendors. But, in practice, it is impractical to
collect a sufficient amount of diverse data for training. To this end, a novel
contrastive learning is developed to equip the deep learning models with better
style generalization capability. Specifically, the multi-style and multi-view
unsupervised self-learning scheme is carried out to seek robust feature
embedding against style diversity as a pretrained model. Afterward, the
pretrained network is further fine-tuned to the downstream tasks, e.g., mass
detection, matching, BI-RADS rating, and breast density classification. The
proposed method has been evaluated extensively and rigorously with mammograms
from various vendor style domains and several public datasets. The experimental
results suggest that the proposed domain generalization method can effectively
improve performance of four mammographic image tasks on the data from both seen
and unseen domains, and outperform many state-of-the-art (SOTA) generalization
methods.
