  In recent years, many mammographic image analysis methods have been
introduced for improving cancer classification tasks. Two major issues of
mammogram classification tasks are leveraging multi-view mammographic
information and class-imbalance handling. In the first problem, many multi-view
methods have been released for concatenating features of two or more views for
the training and inference stage. Having said that, most multi-view existing
methods are not explainable in the meaning of feature fusion, and treat many
views equally for diagnosing. Our work aims to propose a simple but novel
method for enhancing examined view (main view) by leveraging low-level feature
information from the auxiliary view (ipsilateral view) before learning the
high-level feature that contains the cancerous features. For the second issue,
we also propose a simple but novel malignant mammogram synthesis framework for
upsampling minor class samples. Our easy-to-implement and no-training framework
has eliminated the current limitation of the CutMix algorithm which is
unreliable synthesized images with random pasted patches, hard-contour
problems, and domain shift problems. Our results on VinDr-Mammo and CMMD
datasets show the effectiveness of our two new frameworks for both multi-view
training and synthesizing mammographic images, outperforming the previous
conventional methods in our experimental settings.
