Nuclei Detection Using Residual Attention Feature Pyramid Networks

Detection of cell nuclei in microscopy images is a challenging research topic due to limitations in acquired image quality as well as due to the diversity of nuclear morphology. This has been a topic of enduring interest with promising success shown by deep learning methods. Recently, attention gating methods have been proposed and employed successfully in a diverse array of pattern recognition tasks. In this work, we introduce a novel attention module and integrate it with feature pyramid networks and the state-of-the-art Mask R-CNN network. We show with numerical experiments that the proposed model outperforms the state-of-the-art baseline.