From single to multiple: Generalized detection of Covid-19 under limited classes samples

Amid the unfolding Covid-19 pandemic, there is a critical need for rapid and accurate diagnostic methods. In this context, the field of deep learning-based medical image diagnosis has witnessed a swift evolution. However, the prevailing methodologies often rely on large amounts of labeled data and require comprehensive medical knowledge. Both of these prerequisites pose significant challenges in real clinical settings, given the high cost of data labeling and the complexities of disease representations. Addressing this gap, we propose a novel problem setting, the Open-Set Single-Domain Generalization for Medical Image Diagnosis (OSSDG-MID). In OSSDG-MID, our aim is to train a model exclusively on a single source domain, so it can classify samples from the target domain accurately, designating them as 'unknown' if they don't belong to the source domain sample category space. Our innovative solution, the Multiple Cross-Matching method (MCM), enhances the identification of these 'unknown' categories by generating auxiliary samples that fall outside the category space of the source domain. Experimental evaluations on two diverse cross-domain image classification tasks demonstrate that our approach outperforms existing methodologies in both single-domain generalization and open-set image classification.