Nobutoshi Nawa, Hisaaki Nishimura, Yusuke Matsuyama, Satomi Doi, Aya Isumi, Takeo Fujiwara
Association between the number of convenience stores and caries status in permanent teeth among elementary school children: results from the A-CHILD population-based longitudinal cohort study
Frontiers in public health
purpose study establish deep learning automatic assistance diagnosis system benign malignant classification mediastinal lesions endobronchial ultrasound ebus images ebus images form video contain multiple imaging modes different imaging modes different frames reflect different characteristics lesions compared previous studies proposed model efficiently extract integrate spatiotemporal relationships different modes require manual selection representative frames recent years vision transformer received much attention field computer vision combined convolutional neural networks hybrid transformers also perform well small datasets study designed novel deep learning architecture based hybrid transformer called transebus adding learnable parameters temporal dimension transebus able extract spatiotemporal features insufficient data addition designed twostream module integrate information three different imaging modes ebus furthermore applied contrastive learning training transebus enabling learn discriminative representation benign malignant mediastinal lesions results show transebus achieved diagnostic accuracy 82 area curve 08812 test dataset outperforming methods also shows several models improve performance incorporating twostream module proposed system shown potential help physicians distinguishing benign malignant mediastinal lesions thereby ensuring accuracy ebus examination