A Review of The Applications of Deep Learning In the Treatment of Glioma
胶质瘤
计算机科学
医学
癌症研究
作者
Xiao-qiu Yang,Zhuojian Yang,Qianer Xu,Weiji He
标识
DOI:10.1145/3644116.3644150
摘要
Glioma is a malignant tumor originating from glial cells in the brain. It is characterized by high disability rates, poor prognosis, frequent recurrence, and short overall survival. Despite some major discoveries in the past few decades, the cure rate and survival rate of glioma remain low. Therefore, finding new methods and technologies to improve the diagnosis and treatment of glioma has become a hot research topic. Deep learning (DL) has gained widespread attention and rapid development in recent years and combining deep learning with glioma imaging can effectively improve the detection efficiency and accuracy of gliomas, assisting in formulating personalized treatment plans for clinical doctors. With the continuous advancement of deep learning techniques, it plays a wide-ranging role in the field of medicine. For example, in areas such as image segmentation, grading and typing, and prognosis assessment of gliomas, which opens up new pathways for accurate prediction and personalized treatment of gliomas. This article provides a comprehensive review of the application of deep learning in the treatment of glioma from various perspectives, including theory, technology, and applications.