Research
I'm interested in medical image analysis, machine learning, ultrasound imaging, and computer vision.
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Geometric Transformation Uncertainty for Improving 3D Fetal Brain Pose Prediction from Freehand 2D Ultrasound Videos
Jayroop Ramesh ,
Nicola K Dinsdale ,
The INTERGROWTH-21st Consortium ,
Pak Hei Yeung * ,
Ana I.L. Namburete *
International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) , 2024 (Oral)
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An uncertainty-aware deep learning model for automated 3D plane localization in 2D fetal brain images.
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Is Your Style Transfer Doing Anything Useful? An Investigation Into Hippocampus Segmentation and the Role of Preprocessing
Hoda Kalabizadeh,
Ludovica Griffanti,
Pak Hei Yeung ,
Natalie Voets,
Grace Gillis,
Clare Mackay
Ana I.L. Namburete
Nicola K Dinsdale
Konstantinos Kamnitsas
MICCAI Machine Learning in Clinical Neuroimaging Workshop (MLCN) , 2024
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Investigating the importance of intensity normalisation methods in MRI segmentation.
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Sensorless volumetric reconstruction of fetal brain freehand ultrasound scans with deep implicit representation
Pak Hei Yeung ,
Linde Hesse ,
Moska Aliasi ,
Monique Haak ,
The INTERGROWTH-21st Consortium ,
Weidi Xie * ,
Ana I.L. Namburete *
Medical Image Analysis, Volume 94 , 2024 (Impact factor ∼ 11)
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Reconstructing a 3D volume from 2D freehand ultrasound images by training a deep network to implicitly represent the volume.
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Adaptive 3D Localization of 2D Freehand Ultrasound Brain Images
Pak Hei Yeung ,
Moska Aliasi ,
The INTERGROWTH-21st Consortium ,
Weidi Xie ,
Ana I.L. Namburete
International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) , 2022
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Sensorless 3D localization of 2D freehand scans. Trained with just 3D volumes, the model can be adapted to any domains (e.g. machines) in an unsupervised manner.
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ImplicitVol: Sensorless 3D Ultrasound Reconstruction with Deep Implicit Representation
Pak Hei Yeung ,
Linde Hesse ,
Moska Aliasi ,
Monique Haak ,
The INTERGROWTH-21st Consortium ,
Weidi Xie * ,
Ana I.L. Namburete *
Arxiv preprint , 2021
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Reconstructing a 3D volume from 2D freehand ultrasound images by training a deep network to implicitly represent the volume.
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Sli2Vol: Annotate a 3D Volume from a Single Slice with Self-Supervised Learning
Pak Hei Yeung ,
Ana I.L. Namburete * ,
Weidi Xie *
International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) , 2021
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Trained with just raw 3D volumes, a single Sli2Vol model can be used to propagate a single-slice annotation to the whole 3D volume, for any structures across different modalities.
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Learning to Map 2D Ultrasound Images into 3D Space with Minimal Human Annotation
Pak Hei Yeung ,
Moska Aliasi ,
Aris T. Papageorghiou ,
Monique Haak ,
Weidi Xie ,
Ana I.L. Namburete
Medical Image Analysis, Volume 70 , 2021 (Impact factor ∼ 11)
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A network, trained with just registered volumes, to predict the 3D location of 2D freehand ultrasound fetal brain images and video.
The template of this webpage is from source code .