The integration of machine learning in magnetic resonance imaging (MRI), specifically in neuroimaging, is proving to be incredibly effective, leading to better diagnostic accuracy, accelerated image analysis, and data-driven insights, which can potentially transform patient care. Deep learning models utilize multiple layers of processing to capture intricate details of complex data, which can then be used on a variety of tasks, including brain tumor classification, segmentation, image synthesis, and registration. Previous research demonstrates high accuracy in tumor segmentation using various model architectures, including nn-UNet and Swin-UNet. U-Mamba, which uses state space modeling, also achieves high accuracy in medical image segmentation. To leverage these models, we propose a deep learning framework that ensembles these state-of-the-art architectures to achieve accurate segmentation and produce finely synthesized images.
@article{arxiv.2411.17617,
title = {An Ensemble Approach for Brain Tumor Segmentation and Synthesis},
author = {Juampablo E. Heras Rivera and Agamdeep S. Chopra and Tianyi Ren and Hitender Oswal and Yutong Pan and Zineb Sordo and Sophie Walters and William Henry and Hooman Mohammadi and Riley Olson and Fargol Rezayaraghi and Tyson Lam and Akshay Jaikanth and Pavan Kancharla and Jacob Ruzevick and Daniela Ushizima and Mehmet Kurt},
journal= {arXiv preprint arXiv:2411.17617},
year = {2024}
}