Biography
Netza is a passionate fellow interested in computing research for global health technologies, including ubiquitous and pervasive strategies to prevent health hazards and the early detection of clinical conditions.
He received a Bachelor of Information Technology with distinction from the National Technological Institute of Mexico in 2009, the same year in which he was awarded first place at a thesis competition celebrated by the National Association of Education Institution in Information Technology.
In 2014 he received a Master of Science degree in Computer Science from the Ensenada Center for Scientific Research and Higher Education. Netza received his Doctor of Philosophy in Computer Engineering with a straight-pass viva honour from Ulster University in 2021. Following this, he joined the Institute of Biomedical Engineering at the University of Oxford, where he was appointed a Postdoctoral Research Fellowship.
Most Recent Publications
A comprehensive scoping review on machine learning-based fetal echocardiography analysis.
A comprehensive scoping review on machine learning-based fetal echocardiography analysis.
Detection of fetal congenital heart defects on three-vessel view ultrasound videos
Detection of fetal congenital heart defects on three-vessel view ultrasound videos
Abstracts of the 34th World Congress on Ultrasound in Obstetrics and Gynecology, 15-18 September 2024, Budapest, Hungary.
Abstracts of the 34th World Congress on Ultrasound in Obstetrics and Gynecology, 15-18 September 2024, Budapest, Hungary.
Review of Federated Learning and Machine Learning-Based Methods for Medical Image Analysis
Review of Federated Learning and Machine Learning-Based Methods for Medical Image Analysis
OP06.07: Machine learning‐based detection of fetal anatomical orientation in second trimester ultrasound images
OP06.07: Machine learning‐based detection of fetal anatomical orientation in second trimester ultrasound images
Research Interests
• Ubiquitous and pervasive computing
• Global health technologies
• Transfer learning
• Federated learning
• Neural network modelling
Research Groups
Most Recent Publications
A comprehensive scoping review on machine learning-based fetal echocardiography analysis.
A comprehensive scoping review on machine learning-based fetal echocardiography analysis.
Detection of fetal congenital heart defects on three-vessel view ultrasound videos
Detection of fetal congenital heart defects on three-vessel view ultrasound videos
Abstracts of the 34th World Congress on Ultrasound in Obstetrics and Gynecology, 15-18 September 2024, Budapest, Hungary.
Abstracts of the 34th World Congress on Ultrasound in Obstetrics and Gynecology, 15-18 September 2024, Budapest, Hungary.
Review of Federated Learning and Machine Learning-Based Methods for Medical Image Analysis
Review of Federated Learning and Machine Learning-Based Methods for Medical Image Analysis
OP06.07: Machine learning‐based detection of fetal anatomical orientation in second trimester ultrasound images
OP06.07: Machine learning‐based detection of fetal anatomical orientation in second trimester ultrasound images
Publications
Most Recent Publications
A comprehensive scoping review on machine learning-based fetal echocardiography analysis.
A comprehensive scoping review on machine learning-based fetal echocardiography analysis.
Detection of fetal congenital heart defects on three-vessel view ultrasound videos
Detection of fetal congenital heart defects on three-vessel view ultrasound videos
Abstracts of the 34th World Congress on Ultrasound in Obstetrics and Gynecology, 15-18 September 2024, Budapest, Hungary.
Abstracts of the 34th World Congress on Ultrasound in Obstetrics and Gynecology, 15-18 September 2024, Budapest, Hungary.
Review of Federated Learning and Machine Learning-Based Methods for Medical Image Analysis
Review of Federated Learning and Machine Learning-Based Methods for Medical Image Analysis
OP06.07: Machine learning‐based detection of fetal anatomical orientation in second trimester ultrasound images
OP06.07: Machine learning‐based detection of fetal anatomical orientation in second trimester ultrasound images
