most citedImproving Deep Learning Models for Pediatric Low-Grade Glioma Tumors Molecular Subtype Identification Using 3D Probability Distributions of Tumor Location

2 citations · 2 across the 2 of their papers we have counts for

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2 papers

cs.CV2022★ 2 cited

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…

eess.IV2021

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…