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Search results (46)

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Beyond benchmarks of IUGC: Rethinking requirements of deep learning method for intrapartum ultrasound biometry from fetal ultrasound videos.

Bai J. et al, (2026), Medical image analysis, 111

Normative growth trajectories of fetal brain regions validated by satisfactory maturation of neurodevelopmental domains at 2 years of age

Wyburd MK. et al, (2026), Nature Communications, 17

Semi-supervised 3D Medical Segmentation from 2D Natural Images Pretrained Model

Yeung PH. et al, (2026), Lecture Notes in Computer Science, 16241 LNCS, 52 - 62

Is Your Style Transfer Doing Anything Useful? An Investigation Into Hippocampus Segmentation and the Role of Preprocessing

Kalabizadeh H. et al, (2024)

An automated method for tendon image segmentation on ultrasound using grey-level co-occurrence matrix features and hidden Gaussian Markov random fields

Scott I. et al, (2024), Computers in Biology and Medicine, 169, 107872 - 107872

The impact of projected increases in obesity prevalence on incident liver disease in the UK: Insights from Bayesian-network modelling

Waddell TH. et al, (2023), ACM International Conference Proceeding Series, 44 - 48

SFHarmony: Source Free Domain Adaptation for Distributed Neuroimaging Analysis

Dinsdale NK. et al, (2023), 2023 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2023), 11460 - 11471

Challenges for machine learning in clinical translation of big data imaging studies

Dinsdale NK. et al, (2022), Neuron, 110, 3866 - 3881

BEAN: Brain Extraction and Alignment Network for 3D Fetal Neurosonography

Moser F. et al, (2022), NeuroImage, 258, 119341 - 119341

The impact of transfer learning on 3D deep learning convolutional neural network segmentation of the hippocampus in mild cognitive impairment and Alzheimer disease subjects.

Balboni E. et al, (2022), Human brain mapping, 43, 3427 - 3438

Subcortical segmentation of the fetal brain in 3D ultrasound using deep learning

Hesse LS. et al, (2022), NeuroImage, 254, 119117 - 119117

Learning to map 2D ultrasound images into 3D space with minimal human annotation.

Yeung P-H. et al, (2021), Medical image analysis, 70

Deep learning-based unlearning of dataset bias for MRI harmonisation and confound removal.

Dinsdale NK. et al, (2021), NeuroImage, 228

The association between flow and oxygenation and cortical development in fetuses with congenital heart defects using a brain-age prediction algorithm.

Everwijn SMP. et al, (2021), Prenatal diagnosis, 41, 43 - 51

STAMP: Simultaneous Training and Model Pruning for Low Data Regimes in Medical Image Segmentation

Dinsdale N. et al, (2021)

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