2 citations · 7 across the 6 of their papers we have counts for
4 papers · 1 filter
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…
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 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…