深度残差网络在脑肿瘤MRI分类上的研究进展  

Advances in deep residual networks for MRI classification of brain tumors

作　　者：李光宇 杨锋 张智悦 陈雷[2] LI Guangyu;YANG Feng;ZHANG Zhiyue;CHEN Lei(School of Medical Information Engineering,Shandong University of Chinese Medicine,Jinan 250355,China;Assets and Equipment Department,the Affiliated Hospital of Shandong University of Chinese Medicine,Jinan 250014,China)

机构地区：[1]山东中医药大学医学信息工程学院,济南250355 [2]山东中医药大学附属医院资产设备处,济南250014

出　　处：《磁共振成像》2025年第3期143-149,161,共8页Chinese Journal of Magnetic Resonance Imaging

摘　　要：脑肿瘤作为一组在人脑内部或周围异常增殖的组织,其生长可能导致严重的神经功能障碍,对患者的生活质量和生命安全构成重大威胁。因此,准确地对脑肿瘤进行分类,对于制订针对性的治疗方案和评估患者的预后情况具有至关重要的意义。近年来,深度学习技术的迅猛发展为医学影像分析领域开辟了新的途径,深度残差网络(ResNet)及其衍生变体在图像分类任务中展现出了卓越的性能,为脑肿瘤MRI分类带来了新的突破。本文深入探讨了基于深度残差网络的网络模型在脑肿瘤MRI分类中的优化策略,首先介绍了深度残差网络的发展,随后详细地分析了当前深度残差网络及其衍生变体在脑肿瘤磁共振图像上的应用。最后,指出了该领域目前面临的挑战,并对未来的研究方向进行了展望,旨在为相关研究提供全面的参考和思路,推动深度残差网络在脑肿瘤MRI分类中的进一步发展和应用,从而提高脑肿瘤诊断的准确性和效率,为临床治疗提供更有力的支持。Brain tumors,as a group of tissues that proliferate abnormally in or around the human brain,may grow in ways that lead to severe neurological dysfunction,posing a significant threat to patients' quality of life and life safety.Therefore,accurately classifying brain tumors is of crucial importance for formulating targeted treatment plans and evaluating the prognosis of patients.In recent years,the rapid development of deep learning technology has opened up new avenues in the field of medical image analysis,and the deep residual network(ResNet) and its derived variants have demonstrated excellent performance in image classification tasks,bringing new breakthroughs in brain tumor MRI classification.In this paper,the optimization strategy of the network model based on deep residual networks in brain tumor MRI classification is discussed in depth,firstly,the development of deep residual networks is introduced,followed by a detailed analysis of the current applications of deep residual networks and their derived variants on brain tumor MRI images.Finally,the current challenges faced in this field are pointed out,and the future research directions are prospected,aiming to provide comprehensive references and ideas for related research,and to promote the further development and application of deep residual networks in brain tumor MRI classification,so as to improve the accuracy and efficiency of brain tumor diagnosis,and to provide more powerful support for clinical treatment.

关 键 词：深度残差网络 脑肿瘤 磁共振成像 图像分类 注意力机制 迁移学习 

分 类 号：R445.2[医药卫生—影像医学与核医学] R739.4[医药卫生—诊断学]