20 citations · 46 across the 11 of their papers we have counts for
11 papers
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…
In the Picture: Medical Imaging Datasets, Artifacts, and their Living Review
Amelia Jiménez-Sánchez, Natalia-Rozalia Avlona, Sarah de Boer +26
Datasets play a critical role in medical imaging research, yet issues such as label quality, shortcuts, and metadata are often overlooked. This lack of attention may harm the gener…
Efficient MedSAMs: Segment Anything in Medical Images on Laptop
Jun Ma, Feifei Li, Sumin Kim +79
Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive co…
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…
Intrapartum Ultrasound Image Segmentation of Pubic Symphysis and Fetal Head Using Dual Student-Teacher Framework with CNN-ViT Collaborative Learning
Jianmei Jiang, Huijin Wang, Jieyun Bai +4
The segmentation of the pubic symphysis and fetal head (PSFH) constitutes a pivotal step in monitoring labor progression and identifying potential delivery complications. Despite t…
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…
Layer Ensembles: A Single-Pass Uncertainty Estimation in Deep Learning for Segmentation
Kaisar Kushibar, Víctor Manuel Campello, Lidia Garrucho Moras +3
Uncertainty estimation in deep learning has become a leading research field in medical image analysis due to the need for safe utilisation of AI algorithms in clinical practice. Mo…
Domain generalization in deep learning for contrast-enhanced imaging
Carla Sendra-Balcells, Víctor M. Campello, Carlos Martín-Isla +5
The domain generalization problem has been widely investigated in deep learning for non-contrast imaging over the last years, but it received limited attention for contrast-enhance…
Style Curriculum Learning for Robust Medical Image Segmentation
Zhendong Liu, Van Manh, Xin Yang +6
The performance of deep segmentation models often degrades due to distribution shifts in image intensities between the training and test data sets. This is particularly pronounced…