1 citations · 3 across the 11 of their papers we have counts for
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Shadow Reduction in Ultrasound Imaging Using Differentiable Simulation and Radiance Field Decomposition
Valentin Bacher, Pak Hei Yeung, Bernhard Kainz +4
Acoustic shadows from bone and other highly attenuating tissues obscure clinically important structures in ultrasound. In fetal brain imaging, skull-induced artefacts disproportion…
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…
Unsupervised Domain Adaptation via Content Alignment for Hippocampus Segmentation
Hoda Kalabizadeh, Ludovica Griffanti, Pak-Hei Yeung +3
Deep learning models for medical image segmentation often struggle when deployed across different datasets due to domain shifts - variations in both image appearance, known as styl…
Exploring Test Time Adaptation for Subcortical Segmentation of the Fetal Brain in 3D Ultrasound
Joshua Omolegan, Pak Hei Yeung, Madeleine K. Wyburd +5
Monitoring the growth of subcortical regions of the fetal brain in ultrasound (US) images can help identify the presence of abnormal development. Manually segmenting these regions…
Prototype Learning for Explainable Brain Age Prediction
Linde S. Hesse, Nicola K. Dinsdale, Ana I. L. Namburete
The lack of explainability of deep learning models limits the adoption of such models in clinical practice. Prototype-based models can provide inherent explainable predictions, but…
SFHarmony: Source Free Domain Adaptation for Distributed Neuroimaging Analysis
Nicola K Dinsdale, Mark Jenkinson, Ana IL Namburete
To represent the biological variability of clinical neuroimaging populations, it is vital to be able to combine data across scanners and studies. However, different MRI scanners pr…