🎉 Deep learning and radiomics-based approach to meningioma grading: exploring the potential value of peritumoral edema regions
Summary & Overview
This paper presents the culmination of my master’s research: a significant study on improving the preoperative grading of meningioma, a common brain tumor. This work, recently accepted by the prestigious Physics in Medicine & Biology (PMB) journal, introduces a novel approach that combines radiomics (extracting features from medical images) with deep learning techniques.
We developed a unique Transformer-CNN hybrid model (MFEM) that effectively integrates both local and global image information. A key innovation of our study is the exploration and demonstrated value of peritumoral edema (PTE) regions—the swelling around the tumor—in enhancing grading.
This project is a collaborative effort with my good friend, Dr. Ying Miao from Peking University. I wish her all the best for a smooth and successful graduation!
Article Link
You can find the published article here:
