Global and local attentional feature alignment for domain adaptive nuclei detection in histopathology images

Automated nuclei detection is crucial prerequisites for a number of histopathology related image analysis such as cancer diagnosis. Although existing deep learning based nuclei detection methods have achieved promising results, they cannot effectively deal with domain shift problem caused by different staining procedures and organ specific nuclear morphology. To handle this problem, in this paper a novel adversarial feature alignment method is proposed for domain adaptive nuclei detection, which includes both global alignment and local attentional alignment components to transfer the knowledge from source domain to target domain. Specifically, in local attentional alignment component, by using nuclei locations as guidance we extract local features and perform adversarial alignment. Furthermore, to address the issue that these local features from nuclei regions often contain insufficient information because of the small size of nuclei, we introduce an efficient location-aware self-attention (LocSA) module to refine local features by utilizing cues from all nuclei for obtaining discriminative features to perform successful feature alignment. Extensive experimental results are provided on two adaptation scenarios and our method demonstrates favorable performance against existing domain adaptation methods, which highlights the effectiveness of the proposed method for domain adaptive nuclei detection.