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Netzahualcoyotl Hernandez Cruz

Netzahualcoyotl Hernandez-Cruz BEng MSc PhD

Dr

Postdoctoral Research Fellow

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

Machine learning-based method for the detection of dextrocardia in ultrasound video clips

Machine learning-based method for the detection of dextrocardia in ultrasound video clips

HarmonicEchoNet: Leveraging harmonic convolutions for automated standard plane detection in fetal heart ultrasound videos.

HarmonicEchoNet: Leveraging harmonic convolutions for automated standard plane detection in fetal heart ultrasound videos.

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

View all

Research Interests

• Ubiquitous and pervasive computing
• Global health technologies
• Transfer learning
• Federated learning
• Neural network modelling

ORCiD

Research Groups

Most Recent Publications

Machine learning-based method for the detection of dextrocardia in ultrasound video clips

Machine learning-based method for the detection of dextrocardia in ultrasound video clips

HarmonicEchoNet: Leveraging harmonic convolutions for automated standard plane detection in fetal heart ultrasound videos.

HarmonicEchoNet: Leveraging harmonic convolutions for automated standard plane detection in fetal heart ultrasound videos.

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

View all

Publications

Most Recent Publications

Machine learning-based method for the detection of dextrocardia in ultrasound video clips

Machine learning-based method for the detection of dextrocardia in ultrasound video clips

HarmonicEchoNet: Leveraging harmonic convolutions for automated standard plane detection in fetal heart ultrasound videos.

HarmonicEchoNet: Leveraging harmonic convolutions for automated standard plane detection in fetal heart ultrasound videos.

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

View all