Domain generalization in nematode classification

Abstract

Nematode images captured by different microscopes may appear differently in terms of image color and image quality, resulting in these images laying in different learning domains. This can negatively impact nematode classification via deep learning. In this paper, we propose a local structure invariance guided (LSIG) domain generalization approach to enhance the model generalization of nematode local regions in unseen domains. First, a style transfer method is introduced to synthesize new domain image samples from the source domain. Unlike in the original input images, the color information of the synthetic images is changed, but their structural information is retained. Then, a metric learning strategy is designed to determine the cross-domain invariant structural representation between the source and new domains by pairwise learning. Each class is then effectively clustered, and a better decision boundary is determined to improve the model generalization. Overall, we demonstrate the effectiveness and robustness of the method on binary-class and multi-class classification tasks on diverse nematode datasets.