World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
60
Citations
13803
World Ranking
2181
National Ranking
146

J. Alison Noble publication distribution in Engineering and Technology in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where J. Alison Noble sits on this spectrum.

38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 135 scientists 78–87 publications: 190 scientists 88–97 publications: 259 scientists 98–107 publications: 283 scientists 108–117 publications: 369 scientists 118–127 publications: 341 scientists 128–137 publications: 386 scientists 138–147 publications: 372 scientists 148–157 publications: 457 scientists 158–167 publications: 415 scientists 168–177 publications: 407 scientists 178–187 publications: 421 scientists 188–197 publications: 378 scientists 198–207 publications: 403 scientists 208–217 publications: 317 scientists 218–227 publications: 346 scientists 228–237 publications: 321 scientists 238–247 publications: 260 scientists 248–257 publications: 280 scientists 258–267 publications: 240 scientists 268–277 publications: 214 scientists 278–287 publications: 242 scientists 288–297 publications: 203 scientists 298–307 publications: 166 scientists 308–317 publications: 154 scientists 318–327 publications: 175 scientists 328–337 publications: 159 scientists 338–347 publications: 99 scientists 348–357 publications: 131 scientists 358–367 publications: 106 scientists 368–377 publications: 118 scientists 378–387 publications: 97 scientists 388–397 publications: 108 scientists 398–407 publications: 82 scientists 408–417 publications: 71 scientists 418–427 publications: 64 scientists 428–437 publications: 55 scientists 438–447 publications: 54 scientists 448–457 publications: 60 scientists 458–467 publications: 47 scientists 468–477 publications: 40 scientists 478–487 publications: 30 scientists 488–497 publications: 29 scientists 498–507 publications: 38 scientists 508–517 publications: 40 scientists 518–527 publications: 32 scientists 528–537 publications: 23 scientists 538–547 publications: 28 scientists 548–557 publications: 23 scientists 558–567 publications: 19 scientists 568–577 publications: 16 scientists 578–587 publications: 17 scientists 588–597 publications: 18 scientists 598–607 publications: 22 scientists 608–617 publications: 15 scientists 618–627 publications: 9 scientists 628–637 publications: 11 scientists 638–647 publications: 21 scientists 648–657 publications: 12 scientists 658–667 publications: 9 scientists 668–677 publications: 11 scientists 678–687 publications: 9 scientists 688–697 publications: 6 scientists 698–707 publications: 14 scientists 708–717 publications: 7 scientists 718–727 publications: 8 scientists 728–737 publications: 10 scientists 738–747 publications: 9 scientists 748–757 publications: 5 scientists 758–767 publications: 5 scientists 768–777 publications: 11 scientists 778–787 publications: 7 scientists 788–797 publications: 2 scientists 798–803 publications: 4 scientists 804+ publications: 100 scientists
38 publications 804+

This scientist: 384 publications — 87th percentile

87% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 804 publications or more.

J. Alison Noble D-index placement in Engineering and Technology in 2026

The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where J. Alison Noble sits on this spectrum.

30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 129 scientists 33 D-Index: 189 scientists 34 D-Index: 200 scientists 35 D-Index: 262 scientists 36 D-Index: 311 scientists 37 D-Index: 312 scientists 38 D-Index: 350 scientists 39 D-Index: 385 scientists 40 D-Index: 348 scientists 41 D-Index: 362 scientists 42 D-Index: 426 scientists 43 D-Index: 380 scientists 44 D-Index: 310 scientists 45 D-Index: 341 scientists 46 D-Index: 301 scientists 47 D-Index: 306 scientists 48 D-Index: 271 scientists 49 D-Index: 246 scientists 50 D-Index: 210 scientists 51 D-Index: 253 scientists 52 D-Index: 213 scientists 53 D-Index: 221 scientists 54 D-Index: 195 scientists 55 D-Index: 186 scientists 56 D-Index: 170 scientists 57 D-Index: 167 scientists 58 D-Index: 166 scientists 59 D-Index: 144 scientists 60 D-Index: 152 scientists 61 D-Index: 141 scientists 62 D-Index: 138 scientists 63 D-Index: 131 scientists 64 D-Index: 118 scientists 65 D-Index: 114 scientists 66 D-Index: 119 scientists 67 D-Index: 95 scientists 68 D-Index: 87 scientists 69 D-Index: 77 scientists 70 D-Index: 89 scientists 71 D-Index: 69 scientists 72 D-Index: 54 scientists 73 D-Index: 46 scientists 74 D-Index: 55 scientists 75 D-Index: 54 scientists 76 D-Index: 49 scientists 77 D-Index: 53 scientists 78 D-Index: 46 scientists 79 D-Index: 28 scientists 80 D-Index: 39 scientists 81 D-Index: 36 scientists 82 D-Index: 24 scientists 83 D-Index: 26 scientists 84 D-Index: 36 scientists 85 D-Index: 18 scientists 86 D-Index: 25 scientists 87 D-Index: 19 scientists 88 D-Index: 26 scientists 89 D-Index: 27 scientists 90 D-Index: 23 scientists 91 D-Index: 15 scientists 92 D-Index: 12 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 13 scientists 97 D-Index: 13 scientists 98 D-Index: 9 scientists 99 D-Index: 7 scientists 100 D-Index: 7 scientists 101 D-Index: 8 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 9 scientists 105 D-Index: 6 scientists 106 D-Index: 9 scientists 107+ D-Index: 99 scientists
30 D-Index 107+

This scientist: 60 D-Index — 78th percentile

78% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 107 D-Index or more.

Research.com Recognitions

  • 2008 - Fellow of the Royal Academy of Engineering (UK)

Overview

J. Alison Noble is affiliated with the University of Oxford in the United Kingdom. Their research spans the fields of Medicine and Computer Science, with a significant focus on Pediatrics, Perinatology and Child Health, as well as Artificial Intelligence and Computer Vision.

Their subfields of study include:

  • Pediatrics, Perinatology and Child Health
  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Radiology, Nuclear Medicine and Imaging
  • Surgery

They have contributed to research in a range of main topics, including:

  • Fetal and Pediatric Neurological Disorders
  • Domain Adaptation and Few-Shot Learning
  • Multimodal Machine Learning Applications
  • Neonatal and fetal brain pathology
  • Prenatal Screening and Diagnostics
  • Artificial Intelligence in Healthcare and Education
  • Radiomics and Machine Learning in Medical Imaging

The scientist has authored important papers, among which are the following recent works:

  • "Introduction to artificial intelligence in ultrasound imaging in obstetrics and gynecology" (2020), published in Ultrasound in Obstetrics and Gynecology
  • "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" (2020), published in The Lancet Digital Health
  • "Assistive artificial intelligence for ultrasound image interpretation in regional anaesthesia: an external validation study" (2022), published in British Journal of Anaesthesia
  • "Exploring the utility of assistive artificial intelligence for ultrasound scanning in regional anesthesia" (2022), published in Regional Anesthesia & Pain Medicine
  • "Transforming obstetric ultrasound into data science using eye tracking, voice recording, transducer motion and ultrasound video" (2021), published in Scientific Reports

Frequently publishing in venues such as:

  • arXiv (Cornell University)
  • Ultrasound in Obstetrics and Gynecology
  • Lecture notes in computer science
  • Medical Image Analysis
  • Journal of Crohn s and Colitis

J. Alison Noble has collaborated frequently with a group of coauthors, including:

  • Aris T. Papageorghiou
  • Lior Drukker
  • Divyanshu Mishra
  • Pramit Saha
  • Netzahualcoyotl Hernandez-Cruz

Their book publications are mainly with Springer Science+Business Media, comprising works such as:

  • "Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis" (2020)
  • "Medical Image Understanding and Analysis" (2020 and 2021 editions)
  • "Simplifying Medical Ultrasound" (2021 and 2022 editions)

They have been recognized with awards including the Fellow of the Royal Academy of Engineering (UK) in 2008.

Best Publications

  • International standards for fetal growth based on serial ultrasound measurements: the Fetal Growth Longitudinal Study of the INTERGROWTH-21st Project

    Aris T Papageorghiou;Eric O Ohuma;Eric O Ohuma;Douglas G Altman;Tullia Todros

  • Microscopy cell counting and detection with fully convolutional regression networks

    Weidi Xie;J. Alison Noble;Andrew Zisserman

  • Finding corners

    J. Alison Noble

  • Weakly-supervised convolutional neural networks for multimodal image registration.

    Yipeng Hu;Yipeng Hu;Marc Modat;Eli Gibson;Wenqi Li

  • Segmentation of ultrasound B-mode images with intensity inhomogeneity correction

    Guofang Xiao;M. Brady;J.A. Noble;Yongyue Zhang

  • Unified Image and Video Saliency Modeling

    Richard Droste;Jianbo Jiao;J. Alison Noble

  • An adaptive segmentation algorithm for time-of-flight MRA data

    D.L. Wilson;J.A. Noble

  • Learning to detect cells using non-overlapping extremal regions

    Carlos Arteta;Victor Lempitsky;J. Alison Noble;Andrew Zisserman

  • 2D+T acoustic boundary detection in echocardiography.

    Miguel Mulet-Parada;J.Alison Noble

  • Evaluation and Comparison of Current Fetal Ultrasound Image Segmentation Methods for Biometric Measurements: A Grand Challenge

    Sylvia Rueda;Sana Fathima;Caroline L. Knight;Mohammad Yaqub

  • Intensity-based 2-D - 3-D registration of cerebral angiograms

    J.H. Hipwell;G.P. Penney;R.A. McLaughlin;K. Rhode

  • Segmentation of ultrasound images: multiresolution 2D and 3D algorithm based on global and local statistics

    Djamal Boukerroui;Atilla Baskurt;J. Alison Noble;Olivier Basset

  • A shape-space-based approach to tracking myocardial borders and quantifying regional left-ventricular function applied in echocardiography

    G. Jacob;J.A. Noble;C. Behrenbruch;A.D. Kelion

  • A novel ultrasound indentation system for measuring biomechanical properties of in vivo soft tissue.

    Lianghao Han;J.Alison Noble;Michael Burcher

  • Interactive Object Counting

    Carlos Arteta;Victor S. Lempitsky;J. Alison Noble;Andrew Zisserman

  • On the Choice of Band-Pass Quadrature Filters

    Djamal Boukerroui;J. Alison Noble;Michael Brady

  • Random Forest Classification for Automatic Delineation of Myocardium in Real-Time 3D Echocardiography

    Victor Lempitsky;Michael Verhoek;J. Alison Noble;Andrew Blake

  • Registration of Multiview Real-Time 3-D Echocardiographic Sequences

    Unknown

  • Label-driven weakly-supervised learning for multimodal deformarle image registration

    Yipeng Hu;Marc Modat;Eli Gibson;Nooshin Ghavami

  • Ω-Net (Omega-Net): Fully automatic, multi-view cardiac MR detection, orientation, and segmentation with deep neural networks.

    Davis M. Vigneault;Davis M. Vigneault;Davis M. Vigneault;Weidi Xie;Carolyn Y. Ho;David A. Bluemke

  • Imaging techniques for cardiac strain and deformation: comparison of echocardiography, cardiac magnetic resonance and cardiac computed tomography

    Michael Tee;J Alison Noble;David A Bluemke

  • Velocity estimation in ultrasound images: a block matching approach.

    Djamal Boukerroui;Djamal Boukerroui;J. Alison Noble;Michael Brady

  • Statistical 3D Vessel Segmentation Using a Rician Distribution

    Albert C. S. Chung;J. Alison Noble

  • Fully-automated alignment of 3D fetal brain ultrasound to a canonical reference space using multi-task learning.

    Ana I.L. Namburete;Weidi Xie;Mohammad Yaqub;Andrew Zisserman

  • International Standards for Symphysis-Fundal Height Based on Serial Measurements From the Fetal Growth Longitudinal Study of the INTERGROWTH-21st Project: Prospective Cohort Study in Eight Countries

    Aris T Papageorghiou;Eric O Ohuma;Eric O Ohuma;Michael G Gravett;Jane Hirst

  • Local-phase based 3D boundary detection using monogenic signal and its application to real-time 3-D echocardiography images

    Kashif Rajpoot;Vicente Grau;J. Alison Noble

  • A comparison of a similarity-based and a feature-based 2-D-3-D registration method for neurointerventional use

    R.A. McLaughlin;J. Hipwell;D.J. Hawkes;J.A. Noble

Frequent Co-Authors

Aris T. Papageorghiou
Aris T. Papageorghiou University of Oxford
Stephen Kennedy
Stephen Kennedy University of Oxford
Weidi Xie
Weidi Xie University of Oxford
Michael Brady
Michael Brady University of Oxford
Andrew Zisserman
Andrew Zisserman University of Oxford
Fernando C. Barros
Fernando C. Barros Universidade Católica de Pelotas
Albert C. S. Chung
Albert C. S. Chung Hong Kong University of Science and Technology
David A. Bluemke
David A. Bluemke University of Wisconsin–Madison
David J. Hawkes
David J. Hawkes University College London
Tom Vercauteren
Tom Vercauteren King's College London

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