4 papers
Partial Information Decomposition as a Multi-Contrast 3D MRI Selection Strategy for Resource-Constrained Deep Neural Network Training in Brain Tumor Segmentation
Agamdeep Chopra, Mehmet Kurt
Multi-contrast 3D MRI segmentation can be computationally demanding when all available sequences are used. We evaluate a pre-training Partial Information Decomposition framework th…
SFL-Net: Source-Factorized Latent Representation Learning for Multi-Contrast MRI to Tau-PET Synthesis
Agamdeep S. Chopra, Caitlin Neher, Tianyi Ren +3
Tau positron emission tomography supports Alzheimer's disease staging but is difficult to scale because of tracer, scanner, and radiation constraints. Synthesis from structural MRI…
Here Comes the Explanation: A Shapley Perspective on Multi-contrast Medical Image Segmentation
Tianyi Ren, Juampablo Heras Rivera, Hitender Oswal +4
Deep learning has been successfully applied to medical image segmentation, enabling accurate identification of regions of interest such as organs and lesions. This approach works e…
An Ensemble Approach for Brain Tumor Segmentation and Synthesis
Juampablo E. Heras Rivera, Agamdeep S. Chopra, Tianyi Ren +14
The integration of machine learning in magnetic resonance imaging (MRI), specifically in neuroimaging, is proving to be incredibly effective, leading to better diagnostic accuracy,…