Towards Robust Natural-Looking Mammography Lesion Synthesis on
  Ipsilateral Dual-Views Breast Cancer Analysis
Thanh-Huy Nguyen Quang Hien Kha Thai Ngoc Toan Truong Ba Thinh Lam Ba Hung Ngo Quang Vinh Dinh Nguyen Quoc Khanh Le
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.