5 citations · 7 across the 6 of their papers we have counts for
6 papers
Efficient Ultrasound Image Segmentation with Token-Conditioned Neural Cellular Automata
Fangyijie Wang, Tanya Akumu, Zi Ye +3
Point-of-Care Ultrasound (POCUS) plays an important role in bedside diagnosis and clinical decision-making, particularly in resource-constrained settings. Recent deep learning meth…
Beyond Benchmarks of IUGC: Rethinking Requirements of Deep Learning Methods for Intrapartum Ultrasound Biometry from Fetal Ultrasound Videos
Jieyun Bai, Zihao Zhou, Yitong Tang +60
A substantial proportion (45\%) of maternal deaths, neonatal deaths, and stillbirths occur during the intrapartum phase, with a particularly high burden in low- and middle-income c…
Federated learning in low-resource settings: A chest imaging study in Africa -- Challenges and lessons learned
Jorge Fabila, Lidia Garrucho, Víctor M. Campello +2
This study explores the use of Federated Learning (FL) for tuberculosis (TB) diagnosis using chest X-rays in low-resource settings across Africa. FL allows hospitals to collaborati…
PSFHS Challenge Report: Pubic Symphysis and Fetal Head Segmentation from Intrapartum Ultrasound Images
Jieyun Bai, Zihao Zhou, Zhanhong Ou +26
Segmentation of the fetal and maternal structures, particularly intrapartum ultrasound imaging as advocated by the International Society of Ultrasound in Obstetrics and Gynecology…
Democratizing AI in Africa: FL for Low-Resource Edge Devices
Jorge Fabila, Víctor M. Campello, Carlos Martín-Isla +4
Africa faces significant challenges in healthcare delivery due to limited infrastructure and access to advanced medical technologies. This study explores the use of federated learn…
Generalisability of fetal ultrasound deep learning models to low-resource imaging settings in five African countries
Carla Sendra-Balcells, Víctor M. Campello, Jordina Torrents-Barrena +17
Most artificial intelligence (AI) research have concentrated in high-income countries, where imaging data, IT infrastructures and clinical expertise are plentiful. However, slower…