Abstract

Info

Title:: Multi-label chest X-ray classification using sandglass ladder residual network

Author(s):: FANG Junze; XING Suxia; GUO Zheng; LI Kexian; WANG Yu; School of Artificial Intelligence, Beijng Technology and Business University, Beijng 100048, China

Keywords:: chest X-ray; multi-label classification; convolutional neural network; vision transformer; sandglass convolution

Abstract:: A sandglass ladder residual network (SLRN) is proposed for multi-label chest X-ray classification, thereby improving the accuracy of clinical diagnosis. SLRN consists of 3 key modules: (1) a sandglass convolutional module to simultaneously extract inter-channel and spatial information; (2) a ladder self attention block to achieve different window divisions through shift operations, expand the receptive field, and realize multi-scale feature extraction and fusion; (3) class specific residual attention in the multi-label classification stage to capture the correlation between different labels and the importance of features for accomplishing more accurate classification by adjusting the weights of different features. The proposed model is validated using the IU X-Ray dataset collected by Indiana University and the publicly available Chest XRay14 dataset collected by the National Institutes of Health in the United States; and the results demonstrate that SLRN which combines the advantages of convolutional neural network and vision transformer enables the capture of local features and global correlations in images, better handles long-distance dependencies, and assists doctors in clinical diagnosis.