Multi-view deep classification expects to obtain better classification performance than using a single view. However, due to the uncertainty and inconsistency of data sources, adding data views does not necessarily lead to the performance improvements in multi-view classification. How to avoid worsening classification performance when adding views is crucial for multi-view deep learning but rarely studied. To tackle this limitation, in this paper, we reformulate the multi-view classification problem from the perspective of safe learning and thereby propose a Safe Multi-view Deep Classification (SMDC) method, which can guarantee that the classification performance does not deteriorate when fusing multiple views. In the SMDC method, we dynamically integrate multiple views and estimate the inherent uncertainties among multiple views with different root causes based on evidence theory. Through minimizing the uncertainties, SMDC promotes the evidences from data views for correct classification, and in the meantime excludes the incorrect evidences to produce the safe multi-view classification results. Furthermore, we theoretically prove that in the safe multi-view classification, adding data views will certainly not increase the empirical risk of classification. The experiments on various kinds of multi-view datasets validate that the proposed SMDC method can achieve precise and safe classification results.

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.