Brain Tumor Radiogenomic Classification
Amr Mohamed Mahmoud Rabea Aya Sameh Ehab Kamal
The RSNA-MICCAI brain tumor radiogenomic classification challenge aimed to
predict MGMT biomarker status in glioblastoma through binary classification on
Multi parameter mpMRI scans: T1w, T1wCE, T2w and FLAIR. The dataset is splitted
into three main cohorts: training set, validation set which were used during
training, and the testing were only used during final evaluation. Images were
either in a DICOM format or in Png format. different architectures were used to
investigate the problem including the 3D version of Vision Transformer (ViT3D),
ResNet50, Xception and EfficientNet-B3. AUC was used as the main evaluation
metric and the results showed an advantage for both the ViT3D and the Xception
models achieving 0.6015 and 0.61745 respectively on the testing set. compared
to other results, our results proved to be valid given the complexity of the
task. further improvements can be made through exploring different strategies,
different architectures and more diverse datasets.