深度学习在脑肿瘤MRI图像诊断中的研究进展OA

Brain tumors are highly fatal neurological diseases,for which precise diagnosis is crucial to improving prognosis.Traditional imaging techniques are often limited in detecting small lesions,characterizing tumor heterogeneity,and facilitating quantitative analysis.Deep learning technology provides crucial technical support for overcoming such limitations.This paper systematically reviews deep learning research and applications in brain tumor diagnosis.Firstly,widely used public datasets are summarized,and key evaluation metrics along with their clinical significance are outlined.Secondly,the review focuses on three core tasks.For tumor detection,it systematically compares two-stage and one-stage approaches.For tumor segmentation,it summarizes methods based on 2D slices,3D volumes,and multi-contrast sequences.For tumor classification,it analyzes models from single-network and multi-network architectural perspectives.The evolution of mainstream methods is traced,the defining characteristics of different technical routes are highlighted,and the integration strategies and application prospects of large language models in these domains are discussed.Furthermore,the current status of clinical translation for deep learning models is evaluated.Core challenges are analyzed,and potential pathways toward clinical adoption are outlined.Finally,limitations of existing methods are summarized.Future research directions are proposed from three perspectives:data,model improvement,and clinical application.