58 citations · 118 across the 3 of their papers we have counts for
4 papers
Accelerated and Quantitative 3D Semisolid MT/CEST Imaging using a Generative Adversarial Network (GAN-CEST)
Jonah Weigand-Whittier, Maria Sedykh, Kai Herz +7
Purpose: To substantially shorten the acquisition time required for quantitative 3D chemical exchange saturation transfer (CEST) and semisolid magnetization transfer (MT) imaging a…
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results
Raghav Mehta, Angelos Filos, Ujjwal Baid +89
Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges.…
DeepNeuro: an open-source deep learning toolbox for neuroimaging
Andrew Beers, James Brown, Ken Chang +4
Translating neural networks from theory to clinical practice has unique challenges, specifically in the field of neuroimaging. In this paper, we present DeepNeuro, a deep learning…
Sequential 3D U-Nets for Biologically-Informed Brain Tumor Segmentation
Andrew Beers, Ken Chang, James Brown +5
Deep learning has quickly become the weapon of choice for brain lesion segmentation. However, few existing algorithms pre-configure any biological context of their chosen segmentat…