Glioblastoma MRI Progression Prediction

This project focuses on preprocessing raw MRI data, balancing an imbalanced dataset with autoencoder-assisted methods and SMOTE, and benchmarking deep learning architectures for glioblastoma follow-up prediction.

Current experiments suggest that a Mamba-CNN hybrid performs strongly, with accuracy reaching about 77 percent in the current setting.