Dual-view Correlation Hybrid Attention Network for Robust Holistic
  Mammogram Classification
Zhiwei Wang Junlin Xian Kangyi Liu Xin Li Qiang Li Xin Yang
Mammogram image is important for breast cancer screening, and typically
obtained in a dual-view form, i.e., cranio-caudal (CC) and mediolateral oblique
(MLO), to provide complementary information. However, previous methods mostly
learn features from the two views independently, which violates the clinical
knowledge and ignores the importance of dual-view correlation. In this paper,
we propose a dual-view correlation hybrid attention network (DCHA-Net) for
robust holistic mammogram classification. Specifically, DCHA-Net is carefully
designed to extract and reinvent deep features for the two views, and meanwhile
to maximize the underlying correlations between them. A hybrid attention
module, consisting of local relation and non-local attention blocks, is
proposed to alleviate the spatial misalignment of the paired views in the
correlation maximization. A dual-view correlation loss is introduced to
maximize the feature similarity between corresponding strip-like regions with
equal distance to the chest wall, motivated by the fact that their features
represent the same breast tissues, and thus should be highly-correlated.
Experimental results on two public datasets, i.e., INbreast and CBIS-DDSM,
demonstrate that DCHA-Net can well preserve and maximize feature correlations
across views, and thus outperforms the state-of-the-arts for classifying a
whole mammogram as malignant or not.