27 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- The University of QueenslandAU4 papers
- Stanford UniversityUS3 papers
- Allen Institute for Cell ScienceUS2 papers
- ARC Centre of Excellence in Advanced Molecular ImagingAU2 papers
- Broad InstituteUS2 papers
- European Molecular Biology LaboratoryDE2 papers
- Frederick National Laboratory for Cancer ResearchUS2 papers
- Heidelberg UniversityDE2 papers
- KTH Royal Institute of TechnologySE2 papers
- La Trobe UniversityAU2 papers
- Monash UniversityAU2 papers
- National Institute of Standards and TechnologyUS2 papers
4 papers · 1 filter
NeXtQSM -- A complete deep learning pipeline for data-consistent quantitative susceptibility mapping trained with hybrid data
Francesco Cognolato, Kieran O'Brien, Jin Jin +4
Deep learning based Quantitative Susceptibility Mapping (QSM) has shown great potential in recent years, obtaining similar results to established non-learning approaches. Many curr…
A comprehensive solution to retrieval-based chatbot construction
Kristen Moore, Shenjun Zhong, Zhen He +4
In this paper we present the results of our experiments in training and deploying a self-supervised retrieval-based chatbot trained with contrastive learning for assisting customer…
The Federated Tumor Segmentation (FeTS) Challenge
Sarthak Pati, Ujjwal Baid, Maximilian Zenk +29
This manuscript describes the first challenge on Federated Learning, namely the Federated Tumor Segmentation (FeTS) challenge 2021. International challenges have become the standar…
Accelerating Quantitative Susceptibility Mapping using Compressed Sensing and Deep Neural Network
Yang Gao, Martijn Cloos, Feng Liu +3
Quantitative susceptibility mapping (QSM) is an MRI phase-based post-processing method that quantifies tissue magnetic susceptibility distributions. However, QSM acquisitions are r…