Hierarchically engineering quality-related perceptual features for understanding breast cancer

Abstract Breast cancer is generally acknowledged as the second leading cause of cancer death among women. Therefore, accurately understanding breast cancer from X-ray images is an indispensable technique in medical sciences and image analysis. In the work, we propose a novel perceptual deep architecture that hierarchically learns deep features from large-scale X-ray images, wherein human visual perception is naturally encoded. More specifically, given a rich number of breast cancer images, we first employ the well-known BING objectness measure to identify all possible visually/semantically salient patches. Due to the relatively huge number of BING object patches, a weakly-supervised ranking algorithm is designed to select high quality object patches according to human visual perception. Subsequently, an aggregation scheme is utilized to derive the deep features of high quality object patches within each brain cancer image. Based on the aggregated deep feature, a multi-class SVM is trained to classify each breast cancer into multiple levels. Extensive comparative studies and visualization results have demonstrated the effectiveness and efficiency of our proposed deep architecture.