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We present, for the first time, a novel deep neural network architecturecalled \dcn with a dual-path connection between the input image and outputclass label for mammogram image processing. This architecture is built uponU-Net, which non-linearly maps the input data into a deep latent space. Onepath of the \dcnn, the locality preserving learner, is devoted tohierarchically extracting and exploiting intrinsic features of the input, whilethe other path, called the conditional graph learner, focuses on modeling theinput-mask correlations. The learned mask is further used to improveclassification results, and the two learning paths complement each other. Byintegrating the two learners our new architecture provides a simple buteffective way to jointly learn the segmentation and predict the class label.Benefiting from the powerful expressive capacity of deep neural networks a morediscriminative representation can be learned, in which both the semantics andstructure are well preserved. Experimental results show that \dcn achieves thebest mammography segmentation and classification simultaneously, outperformingrecent state-of-the-art models.

A Deep DUAL-PATH Network for Improved Mammogram Image Processing