In this paper, we propose a novel multi-view deep learning approach for cervical dysplasia diagnosis (CDD), using multi-views of image data (acetic images and iodine images) from colposcopy. In general, a major challenge to analyzing multi-view medical image data is how to effectively exploit meaningful correlations among such views. We develop a new feature level fusion (FLF) method, which captures comprehensive correlations between the acetic and iodine image views and sufficiently utilizes information from these two views. Our FLF method is based on attention mechanisms and allows one view to assist another view or allows both views to assist mutually to better facilitate feature learning. Specifically, we explore deep networks for two kinds of FLF methods, uni-directional fusion (UFNet) and bi-directional fusion (BFNet). Experimental results show that our methods are effective for characterizing features of cervical lesions and outperform known methods for CDD.
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