Noble, J. Alison

3D fractional moving blood volume (3D-FMBV) demonstrates decreased first trimester placental vascularity in pre-eclampsia but not the term, small for gestation age baby

Collins, S. L., Welsh, A. W., Impey, L., Noble, J. A., & Stevenson, G. N. (2017) · PLOS ONE

Pregnancy and preeclampsia studiesNeurological Complications and Syndromes

Cited by 25

Preterm feeding recommendations are achievable in large-scale research studies

for the International Fetal and Newborn Growth Consortium for the 21st Century (INTERGROWTH-21st), Cheikh Ismail, L., Giuliani, F., Bhat, B. A., Bishop, D., Papageorghiou, A. T., Ochieng, R., Puglia, F., Altman, D. G., Maia-Schlüssel, M., et al. (2016) · BMC Nutrition

Breastfeeding Practices and InfluencesInfant Nutrition and Health

Cited by 19

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

Venturini, L., Papageorghiou, A. T., Noble, J. A., & Namburete, A. I. L. (2020) · Lecture Notes in Computer Science

Medical Image Segmentation TechniquesDomain Adaptation and Few-Shot Learning

Cited by 18

Multi-Modal Learning from Video, Eye Tracking, and Pupillometry for Operator Skill Characterization in Clinical Fetal Ultrasound

Sharma, H., Drukker, L., Papageorghiou, A. T., & Noble, J. A. (2021) · 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)

Ultrasound in Clinical ApplicationsSurgical Simulation and Training

Cited by 15

Inapplicability of fractional moving blood volume technique to standardize Virtual Organ Computer-aided AnaLysis indices for quantified three-dimensional power Doppler

Welsh, A. W., Collins, S. L., Stevenson, G. N., Noble, J. A., & Impey, L. (2012) · Ultrasound in Obstetrics & Gynecology

Ultrasound in Clinical ApplicationsPregnancy and preeclampsia studies

Cited by 15

Task model-specific operator skill assessment in routine fetal ultrasound scanning

Wang, Y., Yang, Q., Drukker, L., Papageorghiou, A., Hu, Y., & Noble, J. A. (2022) · International Journal of Computer Assisted Radiology and Surgery

Artificial Intelligence in Healthcare and EducationUltrasound in Clinical Applications

Cited by 12

$$\mathrm {IterMask^2}$$: Iterative Unsupervised Anomaly Segmentation via Spatial and Frequency Masking for Brain Lesions in MRI

Liang, Z., Guo, X., Noble, J. A., & Kamnitsas, K. (2024) · Lecture Notes in Computer Science

Medical Image Segmentation TechniquesBrain Tumor Detection and Classification

Cited by 10

IterMask3D: Unsupervised anomaly detection and segmentation with test-time iterative mask refinement in 3D brain MRI

Liang, Z., Guo, X., Xu, W., Ibrahim, Y., Voets, N., Pretorius, P. M., Noble, J. A., & Kamnitsas, K. (2026) · Medical Image Analysis

Medical Image Segmentation TechniquesAnomaly Detection Techniques and Applications

Cited by 3

Introduction to Artificial Intelligence in Ultrasound Imaging in Obstetrics and Gynecology

Drukker, L., Noble, J. A., & Papageorghiou, A. T. (2021) · Obstetrical & Gynecological Survey

Artificial Intelligence in Healthcare and EducationAI in cancer detection

Cited by 2

IterMask3D: Unsupervised Anomaly Detection and Segmentation with Test-Time Iterative Mask Refinement in 3D Brain MR

Liang, Z., Guo, X., Xu, W., Ibrahim, Y., Voets, N., Pretorius, P. M., Noble, J. A., & Kamnitsas, K. (2025) · arXiv

Medical Image Segmentation TechniquesAnomaly Detection Techniques and Applications

Cited by 0