Fang Frank Yu

This study uses single-orientation quantitative susceptibility mapping to directly visualize and localize the ventral intermediate nucleus of the thalamus in MRI scans.

This paper develops a deep learning model using T2-w MRI to predict MGMT promoter methylation in gliomas, achieving higher accuracy than histological methods.

This paper develops a deep learning model using T2-weighted MRI to classify 1p/19q co-deletion status in brain gliomas with high accuracy.

This study evaluates motion artifact effects on glioma marker classification using deep learning and develops a motion correction network to restore classification accuracy.

The paper withdraws its manuscript due to errors in analysis and requests it not be cited.

The paper develops and ensembles 2D/3D segmentation models for brain tumors using multiresolution inputs, achieving high DICE scores and survival prediction accuracy on BRATS 2019 data.