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
Prenatal detection of congenital heart defects using the deep learning-based image and video analysis: protocol for Clinical Artificial Intelligence in Fetal Echocardiography (CAIFE), an international multicentre multidisciplinary study.
Prenatal detection of congenital heart defects using the deep learning-based image and video analysis: protocol for Clinical Artificial Intelligence in Fetal Echocardiography (CAIFE), an international multicentre multidisciplinary study.
TIER-LOC: Visual Query-based Video Clip Localization in fetal ultrasound videos with a multi-tier transformer.
TIER-LOC: Visual Query-based Video Clip Localization in fetal ultrasound videos with a multi-tier transformer.
Artificial intelligence-enabled prenatal ultrasound for the detection of fetal cardiac abnormalities: a systematic review and meta-analysis
Artificial intelligence-enabled prenatal ultrasound for the detection of fetal cardiac abnormalities: a systematic review and meta-analysis
MCAT: Visual Query-Based Localization of Standard Anatomical Clips in Fetal Ultrasound Videos Using Multi-Tier Class-Aware Token Transformer
MCAT: Visual Query-Based Localization of Standard Anatomical Clips in Fetal Ultrasound Videos Using Multi-Tier Class-Aware Token Transformer
Self-supervised Normality Learning and Divergence Vector-guided Model Merging for Zero-shot Congenital Heart Disease Detection in Fetal Ultrasound Videos
Self-supervised Normality Learning and Divergence Vector-guided Model Merging for Zero-shot Congenital Heart Disease Detection in Fetal Ultrasound Videos
Research Interests
• Ubiquitous and pervasive computing
• Global health technologies
• Transfer learning
• Federated learning
• Neural network modelling
Research Groups
Most Recent Publications
Prenatal detection of congenital heart defects using the deep learning-based image and video analysis: protocol for Clinical Artificial Intelligence in Fetal Echocardiography (CAIFE), an international multicentre multidisciplinary study.
Prenatal detection of congenital heart defects using the deep learning-based image and video analysis: protocol for Clinical Artificial Intelligence in Fetal Echocardiography (CAIFE), an international multicentre multidisciplinary study.
TIER-LOC: Visual Query-based Video Clip Localization in fetal ultrasound videos with a multi-tier transformer.
TIER-LOC: Visual Query-based Video Clip Localization in fetal ultrasound videos with a multi-tier transformer.
Artificial intelligence-enabled prenatal ultrasound for the detection of fetal cardiac abnormalities: a systematic review and meta-analysis
Artificial intelligence-enabled prenatal ultrasound for the detection of fetal cardiac abnormalities: a systematic review and meta-analysis
MCAT: Visual Query-Based Localization of Standard Anatomical Clips in Fetal Ultrasound Videos Using Multi-Tier Class-Aware Token Transformer
MCAT: Visual Query-Based Localization of Standard Anatomical Clips in Fetal Ultrasound Videos Using Multi-Tier Class-Aware Token Transformer
Self-supervised Normality Learning and Divergence Vector-guided Model Merging for Zero-shot Congenital Heart Disease Detection in Fetal Ultrasound Videos
Self-supervised Normality Learning and Divergence Vector-guided Model Merging for Zero-shot Congenital Heart Disease Detection in Fetal Ultrasound Videos
Publications
Most Recent Publications
Prenatal detection of congenital heart defects using the deep learning-based image and video analysis: protocol for Clinical Artificial Intelligence in Fetal Echocardiography (CAIFE), an international multicentre multidisciplinary study.
Prenatal detection of congenital heart defects using the deep learning-based image and video analysis: protocol for Clinical Artificial Intelligence in Fetal Echocardiography (CAIFE), an international multicentre multidisciplinary study.
TIER-LOC: Visual Query-based Video Clip Localization in fetal ultrasound videos with a multi-tier transformer.
TIER-LOC: Visual Query-based Video Clip Localization in fetal ultrasound videos with a multi-tier transformer.
Artificial intelligence-enabled prenatal ultrasound for the detection of fetal cardiac abnormalities: a systematic review and meta-analysis
Artificial intelligence-enabled prenatal ultrasound for the detection of fetal cardiac abnormalities: a systematic review and meta-analysis
MCAT: Visual Query-Based Localization of Standard Anatomical Clips in Fetal Ultrasound Videos Using Multi-Tier Class-Aware Token Transformer
MCAT: Visual Query-Based Localization of Standard Anatomical Clips in Fetal Ultrasound Videos Using Multi-Tier Class-Aware Token Transformer
Self-supervised Normality Learning and Divergence Vector-guided Model Merging for Zero-shot Congenital Heart Disease Detection in Fetal Ultrasound Videos
Self-supervised Normality Learning and Divergence Vector-guided Model Merging for Zero-shot Congenital Heart Disease Detection in Fetal Ultrasound Videos
