Single-Slice T1CE-Based Classification of Necrosis and Tumor Enhancement Using Deep Learning
Authors: Vivian Ho, Abdarrahman Ayyaz, Gian Carlo Baldonado
Faculty Supervisor: Ilmi Yoon
Department: Computer Science
Necrosis and enhancing tumors are hallmarks of aggressive cancers, significantly impacting patient prognosis and treatment options. Traditional methods for diagnosing tumor necrosis and enhancement are time-consuming and inefficient. Our work explores a deep learning approach using a pre-trained UNET model to analyze single slice T1CE MRI scans to automate necrosis and enhance tumor region identification. Through this work, we aim to streamline tumor analysis and potentially aid in treatment decisions.