82 citations
- University of OsloNO8 papers
- Maastricht UniversityNL2 papers
- Norwegian Computing CenterNO2 papers
- Argonne National LaboratoryUS1 paper
- Barcelona Supercomputing CenterES1 paper
- Brookhaven National LaboratoryUS1 paper
- Centre for Development of Advanced ComputingIN1 paper
- Centre Inria de l'Université de RennesFR1 paper
- Crawley HospitalGB1 paper
- DePaul UniversityUS1 paper
- Energy NorwayNO1 paper
- FernUniversität in HagenDE1 paper
8 papers · 1 filter
Workflows Community Summit 2024: Future Trends and Challenges in Scientific Workflows
Rafael Ferreira da Silva, Deborah Bard, Kyle Chard +108
The Workflows Community Summit gathered 111 participants from 18 countries to discuss emerging trends and challenges in scientific workflows, focusing on six key areas: time-sensit…
Deep learning-based shot-domain seismic deblending
Jing Sun, Song Hou, Vetle Vinje +2
To streamline fast-track processing of large data volumes, we have developed a deep learning approach to deblend seismic data in the shot domain based on a practical strategy for g…
A convolutional neural network approach to deblending seismic data
Jing Sun, Sigmund Slang, Thomas Elboth +3
For economic and efficiency reasons, blended acquisition of seismic data is becoming more and more commonplace. Seismic deblending methods are always computationally demanding and…
Multi-Stream Cellular Test-Time Adaptation of Real-Time Models Evolving in Dynamic Environments
Benoît Gérin, Anaïs Halin, Anthony Cioppa +5
In the era of the Internet of Things (IoT), objects connect through a dynamic network, empowered by technologies like 5G, enabling real-time data sharing. However, smart objects, n…
Deep Learning for Multi-Level Detection and Localization of Myocardial Scars Based on Regional Strain Validated on Virtual Patients
Müjde Akdeniz, Claudia Alessandra Manetti, Tijmen Koopsen +6
How well the heart is functioning can be quantified through measurements of myocardial deformation via echocardiography. Clinical assessment of cardiac function is generally focuse…
A Data Augmentation Pipeline to Generate Synthetic Labeled Datasets of 3D Echocardiography Images using a GAN
Cristiana Tiago, Andrew Gilbert, Ahmed S. Beela +3
Due to privacy issues and limited amount of publicly available labeled datasets in the domain of medical imaging, we propose an image generation pipeline to synthesize 3D echocardi…