Aris T. Papageorghiou

Achieving accurate estimates of fetal gestational age and personalised predictions of fetal growth based on data from an international prospective cohort study: a population-based machine learning study

T. de Wet, W. Chumlea, A. Ourmazd, 2020, The Lancet. Digital health.

Uncertainty Estimates as Data Selection Criteria to Boost Omni-Supervised Learning

J. Alison Noble, Ana I. L. Namburete, Aris Papageorghiou, 2020, MICCAI.

Normative spatiotemporal fetal brain maturation with satisfactory development at 2 years

Ana I. L. Namburete, M. Purwar, W. Squier, 2023, Nature.

Gestational weight gain standards based on women enrolled in the Fetal Growth Longitudinal Study of the INTERGROWTH-21st Project: a prospective longitudinal cohort study

J. Hirst, D. Altman, M. Purwar, 2016, British Medical Journal.

Memory-based unsupervised video clinical quality assessment with multi-modality data in fetal ultrasound

L. Drukker, Clare Teng, R. Yasrab, 2023, Medical Image Anal..

Calibrated Bayesian Neural Networks to Estimate Gestational Age and Its Uncertainty on Fetal Brain Ultrasound Images

J. Alison Noble, Lok Hin Lee, Aris Papageorghiou, 2020, ASMUS/PIPPI@MICCAI.

Oriented feature-based coupled ellipse fitting for soft tissue quantification in ultrasound images

J. Alison Noble, Sylvia Rueda, Caroline L. Knight, 2013, 2013 IEEE 10th International Symposium on Biomedical Imaging.

Automating the Human Action of First-Trimester Biometry Measurement from Real-World Freehand Ultrasound.

L. Drukker, R. Yasrab, J. A. Noble, 2024, Ultrasound in medicine & biology.