Towards Robust Dual-View Transformation via Densifying Sparse Supervision for Mammography Lesion Matching

Junlin Xian, Zhiwei Wang, Kwang-Ting Cheng, Xin Yang

Abstract: A holistic understanding of dual-view transformation (DVT) is an enabling technique for computer-aided diagnosis (CAD) of breast lesion in mammogram, e.g., micro-calcification (\(\mu \)C) or mass matching, dual-view feature extraction etc. Learning a complete DVT usually relies on a dense supervision which indicates a corresponding tissue in one view for each tissue in another. Since such dense supervision is infeasible to obtain in practical, a sparse supervision of some traceable lesion tissues across two views is thus an alternative but will lead to a defective DVT, limiting the performance of existing CAD systems dramatically. To address this problem, our solution is simple but very effective, i.e., densifying the existing sparse supervision by synthesizing lesions across two views. Specifically, a Gaussian model is first employed for capturing the spatial relationship of real lesions across two views, guiding a following proposed LT-GAN where to synthesize fake lesions. The proposed novel LT-GAN can not only synthesize visually realistic lesions, but also guarantee appearance consistency across views. At last, a denser supervision can be composed based on both real and synthetic lesions, enabling a robust DVT learning. Experimental results show that a DVT can be learned via our densified supervision, and thus result in a superior performance of cross-view \(\mu \)C matching on INbreast and CBIS-DDSM dataset to the state-of-the-art methods.