测试科学与仪器2025，Vol.16Issue(1)：1-10,10.DOI:10.62756/jmsi.1674-8042.2025001
用于CXR图像中结核病检测的多尺度输入镜像网络
Multi-scale input mirror network for tuberculosis detection in CXR image
摘要
Abstract
Computer-aided diagnosis(CAD)can detect tuberculosis(TB)cases,providing radiologists with more accurate and efficient diagnostic solutions.Various noise information in TB chest X-ray(CXR)images is a major challenge in this classification task.This study aims to propose a model with high performance in TB CXR image detection named multi-scale input mirror network(MIM-Net)based on CXR image symmetry,which consists of a multi-scale input feature extraction network and mirror loss.The multi-scale image input can enhance feature extraction,while the mirror loss can improve the network performance through self-supervision.We used a publicly available TB CXR image classification dataset to evaluate our proposed method via 5-fold cross-validation,with accuracy,sensitivity,specificity,positive predictive value,negative predictive value,and area under curve(AUC)of 99.67%,100%,99.60%,99.80%,100%,and 0.999 9,respectively.Compared to other models,MIM-Net performed best in all metrics.Therefore,the proposed MIM-Net can effectively help the network learn more features and can be used to detect TB in CXR images,thus assisting doctors in diagnosing.关键词
计算机辅助诊断/医学图像分类/深度学习/特征对称/镜像损失函数Key words
computer-aided diagnosis(CAD)/medical image classification/deep learning/feature symmetry/mirror loss引用本文复制引用
邢广鑫,樊晶晶,郑叶龙,赵美蓉..用于CXR图像中结核病检测的多尺度输入镜像网络[J].测试科学与仪器,2025,16(1):1-10,10.基金项目
This work was supported by the Joint Fund of the Ministry of Education for Equipment Pre-research(No.8091B0203),and National Key Research and Development Program of China(No.2020YFC2008700). （No.8091B0203）
