2 citations · 7 across the 6 of their papers we have counts for
6 papers
Tumor Location-weighted MRI-Report Contrastive Learning: A Framework for Improving the Explainability of Pediatric Brain Tumor Diagnosis
Sara Ketabi, Matthias W. Wagner, Cynthia Hawkins +3
Despite the promising performance of convolutional neural networks (CNNs) in brain tumor diagnosis from magnetic resonance imaging (MRI), their integration into the clinical workfl…
Improving Pediatric Low-Grade Neuroepithelial Tumors Molecular Subtype Identification Using a Novel AUROC Loss Function for Convolutional Neural Networks
Khashayar Namdar, Matthias W. Wagner, Cynthia Hawkins +3
Pediatric Low-Grade Neuroepithelial Tumors (PLGNT) are the most common pediatric cancer type, accounting for 40% of brain tumors in children, and identifying PLGNT molecular subtyp…
Generating 3D Brain Tumor Regions in MRI using Vector-Quantization Generative Adversarial Networks
Meng Zhou, Matthias W Wagner, Uri Tabori +3
Medical image analysis has significantly benefited from advancements in deep learning, particularly in the application of Generative Adversarial Networks (GANs) for generating real…
Generative Adversarial Networks for Weakly Supervised Generation and Evaluation of Brain Tumor Segmentations on MR Images
Jay J. Yoo, Khashayar Namdar, Matthias W. Wagner +5
Segmentation of regions of interest (ROIs) for identifying abnormalities is a leading problem in medical imaging. Using machine learning for this problem generally requires manuall…
Improving Deep Learning Models for Pediatric Low-Grade Glioma Tumors Molecular Subtype Identification Using 3D Probability Distributions of Tumor Location
Khashayar Namdar, Matthias W. Wagner, Kareem Kudus +4
Background and Purpose: Pediatric low-grade glioma (pLGG) is the most common type of brain tumor in children, and identification of molecular markers for pLGG is crucial for succes…
Improving the Segmentation of Pediatric Low-Grade Gliomas through Multitask Learning
Partoo Vafaeikia, Matthias W. Wagner, Uri Tabori +2
Brain tumor segmentation is a critical task for tumor volumetric analyses and AI algorithms. However, it is a time-consuming process and requires neuroradiology expertise. While th…