Biography
Alison Noble was an undergraduate and a postgraduate research student at St Hugh’s College, Oxford studying Engineering Science and Computer Vision respectively. She became a research scientist at the GE Corporate R&D Center, Schenectady NY USA following DPhil (PhD) graduation before appointment to the Oxford Engineering Science faculty in 1995. Alison Noble was elected a Professor of Engineering Science in 2002, and the Technikos Professor in Biomedical Engineering in 2011. Professor Alison Noble was the Director of the Institute of Biomedical Engineering from 2012-16 and an Associate Head of the Mathematical, Physical and Life Sciences Division at the University of Oxford, 2016-19.
Alison’s research interests are in ultrasound imaging, computational (machine-learning based) analysis of images, and the development of decision-support tools for medical professionals. Her research is motivated by clinical unmet needs in western and developing world healthcare settings. She co-founded Intelligent Ultrasound Ltd to commercial research from her laboratory which was acquired by MedaPhor Group Plc (now called Intelligent Ultrasound Group) in 2017.
Alison is a former president of the Medical Image Computing and Computer-Assisted Interventions (MICCAI) Society. She has served as a member and then Chair of the EPSRC Healthcare Technologies Strategic Advisory Team (2014-19) and a Trustee of the Institute of Engineering Technology (IET) 2016-19. She is a member of REF 2021 Subpanel 12 (Engineering). Professor Alison Noble is a Fellow of the Royal Academy and of the Royal Society, and a Trustee of the Oxford Trust. She received an OBE for services to science and engineering in the Queen's Birthday Honours list in June 2013.
Publications
View a list of Professor Noble's publications on Google Scholar, or by using her ORCID ID.
Most Recent Publications
Transforming obstetric ultrasound into data science using eye tracking, voice recording, transducer motion and ultrasound video.
Drukker L et al. (2021), Scientific reports, 11(1), 14109
Machine learning-based analysis of operator pupillary response to assess cognitive workload in clinical ultrasound imaging.
Sharma H et al. (2021), Computers in biology and medicine, 135, 104589
Knowledge representation and learning of operator clinical workflow from full-length routine fetal ultrasound scan videos.
Sharma H et al. (2021), Medical image analysis, 69, 101973
The effect of maternal body mass index on fetal ultrasound image quality.
Yaqub M et al. (2021), American journal of obstetrics and gynecology
Multi-modal learning from video, eye tracking, and pupillometry for operator skill characterization in clinical fetal ultrasound
Sharma H et al. (2021), Proceedings - International Symposium on Biomedical Imaging, 2021-April, 1646-1649
Research Interests
- Biomedical imaging and image analysis
- Machine learning applied to healthcare
- Ultrasound image analysis
- Point-of-care ultrasound
- Global health technologies
Professor Noble's collaborators and research partners include:
- Nuffield Department of Women’s and Reproductive Health, University of Oxford
- Visual Geometry Group, University of Oxford
- Translational Health Science and Technology Institute (THSTI), Faridabad, India
- PRECISE Network supported by GCRF
- Collaborative Initiative on Fetal Alcohol Spectrum Disorders (CIFASD) supported by NIAAA
- SmartHeart EPSRC programme grant
Other
For more information about Professor Noble’s research see here.
Most Recent Publications
Transforming obstetric ultrasound into data science using eye tracking, voice recording, transducer motion and ultrasound video.
Drukker L et al. (2021), Scientific reports, 11(1), 14109
Machine learning-based analysis of operator pupillary response to assess cognitive workload in clinical ultrasound imaging.
Sharma H et al. (2021), Computers in biology and medicine, 135, 104589
Knowledge representation and learning of operator clinical workflow from full-length routine fetal ultrasound scan videos.
Sharma H et al. (2021), Medical image analysis, 69, 101973
The effect of maternal body mass index on fetal ultrasound image quality.
Yaqub M et al. (2021), American journal of obstetrics and gynecology
Multi-modal learning from video, eye tracking, and pupillometry for operator skill characterization in clinical fetal ultrasound
Sharma H et al. (2021), Proceedings - International Symposium on Biomedical Imaging, 2021-April, 1646-1649
Enquiries from potential postgraduate research students who have interests in any of my research areas are welcome.
Most Recent Publications
Transforming obstetric ultrasound into data science using eye tracking, voice recording, transducer motion and ultrasound video.
Drukker L et al. (2021), Scientific reports, 11(1), 14109
Machine learning-based analysis of operator pupillary response to assess cognitive workload in clinical ultrasound imaging.
Sharma H et al. (2021), Computers in biology and medicine, 135, 104589
Knowledge representation and learning of operator clinical workflow from full-length routine fetal ultrasound scan videos.
Sharma H et al. (2021), Medical image analysis, 69, 101973
The effect of maternal body mass index on fetal ultrasound image quality.
Yaqub M et al. (2021), American journal of obstetrics and gynecology
Multi-modal learning from video, eye tracking, and pupillometry for operator skill characterization in clinical fetal ultrasound
Sharma H et al. (2021), Proceedings - International Symposium on Biomedical Imaging, 2021-April, 1646-1649
