58 citations · 87 across the 4 of their papers we have counts for
4 papers
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.…
Multi-Resolution 3D CNN for MRI Brain Tumor Segmentation and Survival Prediction
Mehdi Amian, Mohammadreza Soltaninejad
In this study, an automated three dimensional (3D) deep segmentation approach for detecting gliomas in 3D pre-operative MRI scans is proposed. Then, a classi-fication algorithm bas…
MRI Brain Tumor Segmentation using Random Forests and Fully Convolutional Networks
Mohammadreza Soltaninejad, Lei Zhang, Tryphon Lambrou +3
In this paper, we propose a novel learning based method for automated segmentation of brain tumor in multimodal MRI images, which incorporates two sets of machine -learned and hand…
Multimodal MRI brain tumor segmentation using random forests with features learned from fully convolutional neural network
Mohammadreza Soltaninejad, Lei Zhang, Tryphon Lambrou +2
In this paper, we propose a novel learning based method for automated segmenta-tion of brain tumor in multimodal MRI images. The machine learned features from fully convolutional n…