Glioma Subtype Classification

Glioma subtype classification aims to accurately categorize brain tumors based on their molecular characteristics, enabling personalized treatment strategies. Current research heavily utilizes deep learning, employing convolutional neural networks (CNNs), transformers, and generative models like diffusion probabilistic models and GANs, often incorporating multi-modal data (MRI, histology) and transfer learning techniques to improve accuracy and address data scarcity. These advancements are improving the speed and accuracy of glioma subtyping, potentially leading to faster diagnoses and more effective treatment plans, particularly through the integration of rapid, label-free imaging methods. The development of robust and generalizable models is crucial for translating these findings into routine clinical practice.