Professor Ana Namburete
Originally from Mozambique, I took the International Baccalaureate at the United World College of Southern Africa in Swaziland (now Eswatini). I then moved to Canada where I obtained a BASc in Engineering Science from Simon Fraser University. With support from a Commonwealth Scholarship, I read for a DPhil in Engineering Science at the University of Oxford. Following a postdoctoral fellowship funded by a grant from the Bill and Melinda Gates Foundation, I secured a Royal Academy of Engineering (RAEng) Research Fellowship to establish my independent research group at Oxford’s Department of Engineering Science, and I also became an Associate Research Fellow at St Hilda's College.
In 2021, I joined the Department of Computer Science as a Research Lecturer, and Pembroke College as Rokos Fellow and Tutor in Computer Science to re-introduce the subject as an undergraduate option.
I am passionate about diversity and inclusion in academic research, and I am a representative of Oxford in the trans-European NeurotechEU alliance for promoting excellence in brain research.
|
Since 2021 |
Rokos Fellow and Tutor in Computer Science, Pembroke College, University of Oxford |
|
Since 2021 |
Research Lecturer, Department of Computer Science, University of Oxford |
|
2016 – 2021 |
Associate Research Fellow, St Hilda's College, University of Oxford |
|
2016 – 2021 |
Royal Academy of Engineering (RAEng) Research Fellow and Group Leader, Department of Engineering Science, University of Oxford |
|
2015 – 2016 |
Junior Research Fellow, St Hilda's College, University of Oxford |
|
2015 – 2016 |
Postdoctoral Researcher and Principal Investigator, BioMedia Lab, University of Oxford |
|
2015 |
DPhil, Engineering Science, BioMedia Lab, University of Oxford |
|
2011 – 2015 |
Commonwealth Scholar, Department of Engineering Science, University of Oxford |
|
2011 |
BASc, Engineering Science, Simon Fraser University, Canada |
|
2006 |
International Baccalaureate Diploma, UWCSA, Swaziland |
A full list of publications can be found here
- Hesse, L. H., Wyburd, M., Aliasi, the INTERGROWTH-21st Consortium, M., Haak, M., Jenkinson, M., Namburete, A. I. L., “Assessment of Regional Cortical Development through Fissure-Based Gestational Age Estimation in 3D Fetal Ultrasound”, Proc. of the Medical Image Computing and Computer-Assisted Intervention (MICCAI)—Workshop on Perinatal, Preterm, and Paediatric Image Analysis (PIPPI). 2021
- Wyburd, M., Jenkinson, M., Namburete, A. I. L., “TEDS-Net: Enforcing Diffeomorphisms in Spatial Transformers to Guarantee Topology Preservation in Segmentations”, Proc. of the Medical Image Computing and Computer-Assisted Intervention (MICCAI). 2021
- Yeung, P.-H., Namburete, A. I. L., Xie, W., “TEDS-Net: Enforcing Diffeomorphisms in Spatial Transformers to Guarantee Topology Preservation in Segmentations”, Proc. of the Medical Image Computing and Computer-Assisted Intervention (MICCAI). 2021
- Yeung, P.-H., Aliasi, M., Papageorghiou, A. T., Haak, M., Xie, W., Namburete, A. I. L. “Learning to map 2D ultrasound images into 3D space with minimal human annotation”. Medical Image Analysis, 70 (101998), 2021
- Dinsdale, N., Jenkinson, M., Namburete, A. I. L. “Deep learning-based unlearning of dataset bias for MRI harmonisation and confound removal”, NeuroImage, 228 (117689), 2021
- Dinsdale, N., Bluemke, E., Smith, S. M., Arya, Z., Vidaurre, D., Jenkinson, M., Namburete, A. I. L. “Learning patterns of the ageing brain in MRI using deep convolutional networks”, NeuroImage, 224 (117401), 2021
- Jiao, J., Namburete, A. I. L., Papageorghiou, A. T., Noble, J. A., “Self-Supervised Ultrasound to MRI Fetal Brain Image Synthesis”, IEEE Transactions on Medical Imaging, 39 (12), 4413-4424, 2020
- Dinsdale, N. K., Jenkinson, M., Namburete, A. I. L., “Unlearning scanner bias for MRI harmonisation”, Proc. of the Medical Image Computing and Computer-Assisted Intervention (MICCAI). In: Martel et al. (eds), 369-378, 2020
- Venturini, L., Noble, J. A., Papageorghiou, A. T., Namburete, A. I. L., “Uncertainty estimates a data selection criteria to boost omni-supervised learning”, Proc. of the Medical Image Computing and Computer-Assisted Intervention (MICCAI). In: Martel et al. (eds), 689-698, 2020
- Hesse, L. S., Namburete, A. I. L., “Improving U-Net Segmentation with Active Contour Based Label Correction”, Proc. of the Medical Image Analysis and Understanding (MIUA). In: Papiez et al. (eds), 69-81, 2020
- Wyburd, M. K., Jenkinson, M., Namburete, A. I. L., “Cortical Plate Segmentation using CNNs in 3D Fetal Ultrasound”, Proc. of the Medical Image Analysis and Understanding (MIUA). In: Papiez et al. (eds), 56-68, 2020
- Vogt, N., Ridgeway, G., Brady, M., Namburete, A. I. L., “Segmenting Hepatocellular Carcinoma in Multi-Phase CT”, Proc. of the Medical Image Analysis and Understanding (MIUA). In: Papiez et al. (eds), 89-92, 2020
- Dinsdale, N. K., Jenkinson, M., Namburete, A. I. L., “Unlearning Scanner Bias for MRI Harmonisation in Medical Image Segmentation”, Proc. of the Medical Image Analysis and Understanding (MIUA). In: Papiez et al. (eds), 15-25, 2020
- Everwijn, S. M. P., Namburete, A. I. L., van Geloven, N., Jansen, F. A. R., Papageorghiou, A. T., Teunissen, A. K. K., Rozendaal, L., Blom, N. A., van Lith, J. M. M., Haak, M. C., “The association between flow and oxygenation and cortical development in fetuses with congenital heart defects using a brain-age prediction algorithm”, Prenatal Diagnosis, 1-9, 2020
- Vaze, S., Xie, W., Namburete, A. I. L. “Low-Memory CNNs Enabling Real-Time Ultrasound Segmentation Towards Mobile Deployment”, IEEE Journal of Biomedical Health Informatics, 24(4), 1059-1069, 2020
- Namburete, A. I. L., Xie, W., Yaqub, M., Zisserman, A., Noble, J. A., “Fully-automated alignment of 3D fetal brain ultrasound to a canonical reference space using multi-task learning”, Medical Image Analysis, 46 (1), 1-14, 2018
- Huang, R., Namburete, A. I. L., Noble, J. A., “Learning to segment key clinical anatomical structures in fetal neurosonography informed by a region-based descriptor”, Journal of Medical Imaging, 5 (1), 2018
- Namburete, A. I. L., van Kampen, R., Papageorghiou, A. T., Papiez, B. W., “Multi-channel Groupwise Registration to Construct an Ultrasound-Specific Fetal Brain Atlas”, Proc. of the Medical Image Computing and Computer-Assisted Intervention (MICCAI)—Workshop on Perinatal, Preterm, and Paediatric Image Analysis (PIPPI). In: Melbourne et al, 76-86, 2018
- Vaze, S., Namburete, A. I. L., “Segmentation of Fetal Adipose Tissue using Efficient CNNs for Portable Ultrasound”, Proc. of the Medical Image Computing and Computer-Assisted Intervention (MICCAI)—Workshop on Perinatal, Preterm, and Paediatric Image Analysis (PIPPI). In: Melbourne et al, 55-65, 2018
- Huang, R., Noble, J. A., Namburete, A. I. L., “Omni-Supervised Learning: Scaling Up to Large Unlabelled Medical Datasets”, Proc. of the Medical Image Computing and Computer-Assisted Intervention (MICCAI). In: Frangi et al (eds), 572-580, 2018
- Namburete, A. I. L., Xie, W., Noble, J. A., “Robust regression of brain maturation from 3D fetal neurosonography using CRNs”, Proc. of the Medical Image Computing and Computer-Assisted Intervention (MICCAI)—Workshop on Fetal and Infant Imaging (FIFI). In: Melbourne et al, 73-80, 2017
- Namburete, A. I. L., Stebbing, R. V., Yaqub, M., Kemp, B., Papageorghiou, A. T., Noble, J. A., “Learning-based prediction of gestational age from ultrasound images of the fetal brain”, Medical Image Analysis, 21 (1), 72-86, 2015
- Stebbing, R. V., Namburete, A. I. L., Upton, R., Leeson, P., Noble, J. A., “Data-driven shape parameterization for segmentation of the right ventricle from 3D+t echocardiography”, Medical Image Analysis, 21 (1), 29-39, 2015
Professor Ana Namburete
Originally from Mozambique, I took the International Baccalaureate at the United World College of Southern Africa in Swaziland (now Eswatini). I then moved to Canada where I obtained a BASc in Engineering Science from Simon Fraser University. With support from a Commonwealth Scholarship, I read for a DPhil in Engineering Science at the University of Oxford. Following a postdoctoral fellowship funded by a grant from the Bill and Melinda Gates Foundation, I secured a Royal Academy of Engineering (RAEng) Research Fellowship to establish my independent research group at Oxford’s Department of Engineering Science, and I also became an Associate Research Fellow at St Hilda's College.
In 2021, I joined the Department of Computer Science as a Research Lecturer, and Pembroke College as Rokos Fellow and Tutor in Computer Science to re-introduce the subject as an undergraduate option.
I am passionate about diversity and inclusion in academic research, and I am a representative of Oxford in the trans-European NeurotechEU alliance for promoting excellence in brain research.
|
Since 2021 |
Rokos Fellow and Tutor in Computer Science, Pembroke College, University of Oxford |
|
Since 2021 |
Research Lecturer, Department of Computer Science, University of Oxford |
|
2016 – 2021 |
Associate Research Fellow, St Hilda's College, University of Oxford |
|
2016 – 2021 |
Royal Academy of Engineering (RAEng) Research Fellow and Group Leader, Department of Engineering Science, University of Oxford |
|
2015 – 2016 |
Junior Research Fellow, St Hilda's College, University of Oxford |
|
2015 – 2016 |
Postdoctoral Researcher and Principal Investigator, BioMedia Lab, University of Oxford |
|
2015 |
DPhil, Engineering Science, BioMedia Lab, University of Oxford |
|
2011 – 2015 |
Commonwealth Scholar, Department of Engineering Science, University of Oxford |
|
2011 |
BASc, Engineering Science, Simon Fraser University, Canada |
|
2006 |
International Baccalaureate Diploma, UWCSA, Swaziland |
A full list of publications can be found here
- Hesse, L. H., Wyburd, M., Aliasi, the INTERGROWTH-21st Consortium, M., Haak, M., Jenkinson, M., Namburete, A. I. L., “Assessment of Regional Cortical Development through Fissure-Based Gestational Age Estimation in 3D Fetal Ultrasound”, Proc. of the Medical Image Computing and Computer-Assisted Intervention (MICCAI)—Workshop on Perinatal, Preterm, and Paediatric Image Analysis (PIPPI). 2021
- Wyburd, M., Jenkinson, M., Namburete, A. I. L., “TEDS-Net: Enforcing Diffeomorphisms in Spatial Transformers to Guarantee Topology Preservation in Segmentations”, Proc. of the Medical Image Computing and Computer-Assisted Intervention (MICCAI). 2021
- Yeung, P.-H., Namburete, A. I. L., Xie, W., “TEDS-Net: Enforcing Diffeomorphisms in Spatial Transformers to Guarantee Topology Preservation in Segmentations”, Proc. of the Medical Image Computing and Computer-Assisted Intervention (MICCAI). 2021
- Yeung, P.-H., Aliasi, M., Papageorghiou, A. T., Haak, M., Xie, W., Namburete, A. I. L. “Learning to map 2D ultrasound images into 3D space with minimal human annotation”. Medical Image Analysis, 70 (101998), 2021
- Dinsdale, N., Jenkinson, M., Namburete, A. I. L. “Deep learning-based unlearning of dataset bias for MRI harmonisation and confound removal”, NeuroImage, 228 (117689), 2021
- Dinsdale, N., Bluemke, E., Smith, S. M., Arya, Z., Vidaurre, D., Jenkinson, M., Namburete, A. I. L. “Learning patterns of the ageing brain in MRI using deep convolutional networks”, NeuroImage, 224 (117401), 2021
- Jiao, J., Namburete, A. I. L., Papageorghiou, A. T., Noble, J. A., “Self-Supervised Ultrasound to MRI Fetal Brain Image Synthesis”, IEEE Transactions on Medical Imaging, 39 (12), 4413-4424, 2020
- Dinsdale, N. K., Jenkinson, M., Namburete, A. I. L., “Unlearning scanner bias for MRI harmonisation”, Proc. of the Medical Image Computing and Computer-Assisted Intervention (MICCAI). In: Martel et al. (eds), 369-378, 2020
- Venturini, L., Noble, J. A., Papageorghiou, A. T., Namburete, A. I. L., “Uncertainty estimates a data selection criteria to boost omni-supervised learning”, Proc. of the Medical Image Computing and Computer-Assisted Intervention (MICCAI). In: Martel et al. (eds), 689-698, 2020
- Hesse, L. S., Namburete, A. I. L., “Improving U-Net Segmentation with Active Contour Based Label Correction”, Proc. of the Medical Image Analysis and Understanding (MIUA). In: Papiez et al. (eds), 69-81, 2020
- Wyburd, M. K., Jenkinson, M., Namburete, A. I. L., “Cortical Plate Segmentation using CNNs in 3D Fetal Ultrasound”, Proc. of the Medical Image Analysis and Understanding (MIUA). In: Papiez et al. (eds), 56-68, 2020
- Vogt, N., Ridgeway, G., Brady, M., Namburete, A. I. L., “Segmenting Hepatocellular Carcinoma in Multi-Phase CT”, Proc. of the Medical Image Analysis and Understanding (MIUA). In: Papiez et al. (eds), 89-92, 2020
- Dinsdale, N. K., Jenkinson, M., Namburete, A. I. L., “Unlearning Scanner Bias for MRI Harmonisation in Medical Image Segmentation”, Proc. of the Medical Image Analysis and Understanding (MIUA). In: Papiez et al. (eds), 15-25, 2020
- Everwijn, S. M. P., Namburete, A. I. L., van Geloven, N., Jansen, F. A. R., Papageorghiou, A. T., Teunissen, A. K. K., Rozendaal, L., Blom, N. A., van Lith, J. M. M., Haak, M. C., “The association between flow and oxygenation and cortical development in fetuses with congenital heart defects using a brain-age prediction algorithm”, Prenatal Diagnosis, 1-9, 2020
- Vaze, S., Xie, W., Namburete, A. I. L. “Low-Memory CNNs Enabling Real-Time Ultrasound Segmentation Towards Mobile Deployment”, IEEE Journal of Biomedical Health Informatics, 24(4), 1059-1069, 2020
- Namburete, A. I. L., Xie, W., Yaqub, M., Zisserman, A., Noble, J. A., “Fully-automated alignment of 3D fetal brain ultrasound to a canonical reference space using multi-task learning”, Medical Image Analysis, 46 (1), 1-14, 2018
- Huang, R., Namburete, A. I. L., Noble, J. A., “Learning to segment key clinical anatomical structures in fetal neurosonography informed by a region-based descriptor”, Journal of Medical Imaging, 5 (1), 2018
- Namburete, A. I. L., van Kampen, R., Papageorghiou, A. T., Papiez, B. W., “Multi-channel Groupwise Registration to Construct an Ultrasound-Specific Fetal Brain Atlas”, Proc. of the Medical Image Computing and Computer-Assisted Intervention (MICCAI)—Workshop on Perinatal, Preterm, and Paediatric Image Analysis (PIPPI). In: Melbourne et al, 76-86, 2018
- Vaze, S., Namburete, A. I. L., “Segmentation of Fetal Adipose Tissue using Efficient CNNs for Portable Ultrasound”, Proc. of the Medical Image Computing and Computer-Assisted Intervention (MICCAI)—Workshop on Perinatal, Preterm, and Paediatric Image Analysis (PIPPI). In: Melbourne et al, 55-65, 2018
- Huang, R., Noble, J. A., Namburete, A. I. L., “Omni-Supervised Learning: Scaling Up to Large Unlabelled Medical Datasets”, Proc. of the Medical Image Computing and Computer-Assisted Intervention (MICCAI). In: Frangi et al (eds), 572-580, 2018
- Namburete, A. I. L., Xie, W., Noble, J. A., “Robust regression of brain maturation from 3D fetal neurosonography using CRNs”, Proc. of the Medical Image Computing and Computer-Assisted Intervention (MICCAI)—Workshop on Fetal and Infant Imaging (FIFI). In: Melbourne et al, 73-80, 2017
- Namburete, A. I. L., Stebbing, R. V., Yaqub, M., Kemp, B., Papageorghiou, A. T., Noble, J. A., “Learning-based prediction of gestational age from ultrasound images of the fetal brain”, Medical Image Analysis, 21 (1), 72-86, 2015
- Stebbing, R. V., Namburete, A. I. L., Upton, R., Leeson, P., Noble, J. A., “Data-driven shape parameterization for segmentation of the right ventricle from 3D+t echocardiography”, Medical Image Analysis, 21 (1), 29-39, 2015