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Found 7 papers, 0 papers with code
Federated learning in low-resource settings: A chest imaging study in Africa -- Challenges and lessons learned
The study compared locally trained models with a federated model built across all institutions to evaluate FL's real-world feasibility.
In the Picture: Medical Imaging Datasets, Artifacts, and their Living Review
To address this gap, we propose a living review that continuously tracks public datasets and their associated research artifacts across multiple medical imaging applications.
PSFHS Challenge Report: Pubic Symphysis and Fetal Head Segmentation from Intrapartum Ultrasound Images
This challenge aimed to enhance the development of automatic segmentation algorithms at an international scale, providing the largest dataset to date with 5, 101 intrapartum ultrasound images collected from two ultrasound machines across three hospitals from two institutions.
Democratizing AI in Africa: FL for Low-Resource Edge Devices
Africa faces significant challenges in healthcare delivery due to limited infrastructure and access to advanced medical technologies.
Generalisability of fetal ultrasound deep learning models to low-resource imaging settings in five African countries
This framework shows promise for building new AI models generalisable across clinical centres with limited data acquired in challenging and heterogeneous conditions and calls for further research to develop new solutions for usability of AI in countries with less resources.
Domain generalization in deep learning for contrast-enhanced imaging
This calls for new tools for generalizing single-domain, single-center deep learning models across new unseen domains and clinical centers in contrast-enhanced imaging.
Combining Multi-Sequence and Synthetic Images for Improved Segmentation of Late Gadolinium Enhancement Cardiac MRI
Second, a deep learning model is trained for segmentation with different combinations of original, augmented and synthetic sequences.
