Semi-supervised Long-tail Endoscopic Image Classification.

Objective To explore the semi-supervised learning (SSL) algorithm for long-tail endoscopic image classification with limited annotations. Method We explored semi-supervised long-tail endoscopic image classification in HyperKvasir, the largest gastrointestinal public dataset with 23 diverse classes. Semi-supervised learning algorithm FixMatch was applied based on consistency regularization and pseudo-labeling. After splitting the training dataset and the test dataset at a ratio of 4:1, we sampled 20%, 50%, and 100% labeled training data to test the classification with limited annotations. Results The classification performance was evaluated by micro-average and macro-average evaluation metrics, with the Mathews correlation coefficient (MCC) as the overall evaluation. SSL algorithm improved the classification performance, with MCC increasing from 0.8761 to 0.8850, from 0.8983 to 0.8994, and from 0.9075 to 0.9095 with 20%, 50%, and 100% ratio of labeled training data, respectively. With a 20% ratio of labeled training data, SSL improved both the micro-average and macro-average classification performance; while for the ratio of 50% and 100%, SSL improved the micro-average performance but hurt macro-average performance. Through analyzing the confusion matrix and labeling bias in each class, we found that the pseudo-based SSL algorithm exacerbated the classifier's preference for the head class, resulting in improved performance in the head class and degenerated performance in the tail class. Conclusion SSL can improve the classification performance for semi-supervised long-tail endoscopic image classification, especially when the labeled data is extremely limited, which may benefit the building of assisted diagnosis systems for low-volume hospitals. However, the pseudo-labeling strategy may amplify the effect of class imbalance, which hurts the classification performance for the tail class.

Identification of three H1N1 influenza virus groups with natural recombinant genes circulating from 1918 to 2009.

In this study, we identify a recombinant pb1 gene, a recombinant MP segment and a recombinant PA segment. The pb1 gene is recombined from two Eurasia swine H1N1 influenza virus lineages. It belongs to a H1N1 swine clade circulating in Europe and Asia from 1999 to 2009. The mosaic MP segment descends from H7 avian and H1N1 human virus lineages and pertains to a large human H1N1 virus family circulating in Asia, Europe and America from 1918 to 2007. The recombinant PA segment originated from two swine H1N1 lineages is found in a swine H1N1 group prevailing in Asia and Europe from 1999 to 2003. These results collectively falsify the hypothesis that influenza virus do not evolve by homologous recombination. Since recombination not only leads to virus genome diversity but also can alter its host adaptation and pathogenecity; the genetic mechanism should not be neglected in influenza virus surveillance.