Real-world classification tasks often show an extremely imbalanced problem. The extreme imbalance will cause a strong bias that the decision boundary of the classifier is completely dominated by the categories with abundant samples, which are also called the head categories. Current methods have alleviated the imbalanced impact from mainly three aspects: class re-balance, decoupling and domain adaptation. However, the existing criterion with the winner-take-all strategy still leads to the crowding problem in the eigenspace. The head categories with many samples can extract features more accurately, but occupy most of the eigenspace. The tail categories sharing the rest of the narrow eigenspace are too crowded together to accurately extract features. Above these issues, we propose a novel T-distributed spherical metric for equalized eigenspace in the imbalanced classification, which has the following innovations: 1) We design the T-distributed spherical metric, which has the characteristics of high kurtosis. Instead of the winner-take-all strategy, the T-distributed spherical metric produces a high logit only when the extracted feature is close enough to the category center, without a strong bias against other categories. 2) The T-distributed spherical metric is integrated into the classifier, which is able to equalize the eigenspace for alleviating the crowding issue in the imbalanced problem. The equalized eigenspace by the T-distributed spherical classifier is capable of improving the accuracy of the tail categories while maintaining the accuracy of the head, which significantly promotes the intraclass compactness and interclass separability of features. Extensive experiments on large-scale imbalanced datasets verify our method, which shows superior results in the long-tailed CIFAR-100/-10 with the imbalanced ratio IR = 100/50. Our method also achieves excellent results on the large-scale ImageNet-LT dataset and the iNaturalist dataset with various backbones. In addition, we provide a case study of the real clinical classification of pancreatic tumor subtypes with 6 categories. Among them, the largest number of PDAC accounts for 315 cases, and the least CP has only 8 cases. After 4-fold cross-validation, we achieved a top-1 accuracy of 69.04%.

Abstract Precise segmentation of the lung airways is essential for a quantitative assessment of airway diseases. However, because of the complexity of the airway structure and the different thicknesses of the trachea at different positions, it is extremely difficult to segment the fine bronchial structure using chest computed tomography (CT). Traditional lung airway segmentation methods are generally based on the grayscale, geometric shape of the image, or the use of prior knowledge of anatomy. In recent years, deep learning techniques such as fully convolutional neural networks (FCNs) have achieved a great success in the field of image segmentation. Specifically, the symmetric encoder–decoder network represented by U‐Net has achieved high accuracy in many medical image segmentation tasks. In the airway segmentation challenge task of the 4th International Symposium on Image Computing and Digital Medicine (ISICDM 2020), 9 of the 12 teams participating in the final round used the U‐Net network or one of its other forms, obtaining good results for lung airway segmentation. Methods used to improve the segmentation accuracy include attention mechanisms and multiscale feature information fusion. This article provides a detailed description of the methods used by these 12 teams and analyses their results.