58 citations · 113 across the 3 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.…
Deep Cellular Recurrent Network for Efficient Analysis of Time-Series Data with Spatial Information
Lasitha Vidyaratne, Mahbubul Alam, Alexander Glandon +3
Efficient processing of large-scale time series data is an intricate problem in machine learning. Conventional sensor signal processing pipelines with hand engineered feature extra…
Superconducting radio-frequency cavity fault classification using machine learning at Jefferson Laboratory
Chris Tennant, Adam Carpenter, Tom Powers +3
We report on the development of machine learning models for classifying C100 superconducting radio-frequency (SRF) cavity faults in the Continuous Electron Beam Accelerator Facilit…
Survey on Deep Neural Networks in Speech and Vision Systems
Mahbubul Alam, Manar D. Samad, Lasitha Vidyaratne +2
This survey presents a review of state-of-the-art deep neural network architectures, algorithms, and systems in vision and speech applications. Recent advances in deep artificial n…