计算机工程与应用2025，Vol.61Issue(10)：331-340,10.DOI:10.3778/j.issn.1002-8331.2402-0041
CNN-Transformer交互模型预测IgA肾病病理分级
CNN-Transformer Interaction Model Prediction Pathological Stratification of IgA Nephropathy
摘要
Abstract
Renal ultrasound examination is an important non-invasive clinical diagnostic method for IgA nephropathy(IgAN),which can avoid unnecessary renal biopsy and is especially crucial for long-term disease management.However,there is still a huge knowledge gap between ultrasound image analysis and renal biopsy pathology analysis,which makes it difficult to perform accurate IgAN pathology stratification directly from ultrasound images in clinical practice.Thus,IgAN diagnosis is still highly dependent on the analysis of its pathology.In this paper,a CNN-Transformer interaction model is designed by fusing convolutional neural network(CNN)and Transformer to achieve the prediction of IgAN pathology stratification through the automatic parsing of renal ultrasound images,which assists doctors in its diagnosis.In this model,to simulate the doctor observing the local texture morphology of the corticomedullary region of the kidney,a CNN stream is designed by integrating deformable convolution to extract the local features of the kidney.To simulate the observations of the overall tissue morphology of the kidney,a Transformer stream is designed with spatial biased attention to extract the global correlations of the renal tissues.To simulate the alternate observations of renal tissue morphology for comprehensive assessment,an intermediate-term feature interaction strategy and a terminal adaptive fusion strategy are proposed to comprehensively reveal the medical information contained in ultrasound images.The experimental results show that the proposed CNN-Transformer interaction model performs well in IgAN pathology stratification through renal ultrasound images with an accuracy of 0.842,a sensitivity of 0.833,a specificity of 0.876,and an AUC of 0.931,which outperforms several existing deep learning methods for ultrasound images.关键词
IgA肾病/超声/深度学习/混合模型/双向交互/自适应融合Key words
IgA nephropathy/ultrasound/deep learning/hybrid model/bidirectional interaction/adaptive fusion分类
计算机与自动化引用本文复制引用
牛昊天,林宇轩,蔡念,谢依颖,张镭..CNN-Transformer交互模型预测IgA肾病病理分级[J].计算机工程与应用,2025,61(10):331-340,10.基金项目
国家自然科学基金(82172019) （82172019）
广东省基础与应用基础研究基金(2022A1515110162). （2022A1515110162）
