World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
38
Citations
5966
World Ranking
10254
National Ranking
641

Andrew P. King publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Andrew P. King sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 213 publications — 51st percentile

51% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Andrew P. King D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Andrew P. King sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 38 D-Index — 30th percentile

30% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Andrew P. King is affiliated with King's College London in the United Kingdom. Their research predominantly spans the field of Medicine, with significant contributions in related subfields such as Radiology, Nuclear Medicine and Imaging, Cardiology and Cardiovascular Medicine, Artificial Intelligence, Biomedical Engineering, and Pulmonary and Respiratory Medicine.

Their scholarly output includes research on multiple topics within medical imaging and cardiovascular science. Major topics of their work include:

  • Cardiac Imaging and Diagnostics
  • Radiomics and Machine Learning in Medical Imaging
  • Cardiovascular Function and Risk Factors
  • Advanced MRI Techniques and Applications
  • Medical Imaging Techniques and Applications
  • Artificial Intelligence in Healthcare and Education
  • Head and Neck Cancer Studies

Andrew P. King has coauthored extensively with several researchers, including Esther Puyol-Antón, Bram Ruijsink, Reza Razavi, Teresa Guerrero Urbano, and Paul Aljabar.

Their recent papers demonstrate a focus on deep learning applications, cardiovascular imaging, and computational methods related to medical physics. Selected recent publications include:

  • "A Topological Loss Function for Deep-Learning Based Image Segmentation Using Persistent Homology," 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence
  • "Fairness in Cardiac Magnetic Resonance Imaging: Assessing Sex and Racial Bias in Deep Learning-Based Segmentation," 2022, Frontiers in Cardiovascular Medicine
  • "Active training of physics-informed neural networks to aggregate and interpolate parametric solutions to the Navier-Stokes equations," 2021, Journal of Computational Physics
  • "A multi-scale variational neural network for accelerating motion-compensated whole-heart 3D coronary MR angiography," 2020, Magnetic Resonance Imaging
  • "Environmental and genetic predictors of human cardiovascular ageing," 2023, Nature Communications

Frequent publication venues where this researcher contributes include arXiv (Cornell University), Radiotherapy and Oncology, European Heart Journal, Lecture Notes in Computer Science, and bioRxiv (Cold Spring Harbor Laboratory).

Best Publications

  • Respiratory motion models: a review.

    Jamie McClelland;David J. Hawkes;Tobias Schaeffter;Tobias Schaeffter;Andrew P. King;Andrew P. King

  • Semi-supervised learning for network-based cardiac MR image segmentation

    Wenjia Bai;Ozan Oktay;Matthew Sinclair;Hideaki Suzuki

  • Design and evaluation of a system for microscope-assisted guided interventions (MAGI)

    P.J. Edwards;A.P. King;C.R. Maurer;D.A. De Cunha

  • A Topological Loss Function for Deep-Learning based Image Segmentation using Persistent Homology.

    James Clough;Nicholas Byrne;Ilkay Oksuz;Veronika A. Zimmer

  • Fully Automated, Quality-Controlled Cardiac Analysis From CMR: Validation and Large-Scale Application to Characterize Cardiac Function.

    Bram Ruijsink;Bram Ruijsink;Esther Puyol-Antón;Ilkay Oksuz;Matthew Sinclair

  • Left-Ventricle Quantification Using Residual U-Net

    Eric Kerfoot;James R. Clough;Ilkay Öksüz;Jack Lee

  • Thoracic respiratory motion estimation from MRI using a statistical model and a 2-D image navigator.

    Andrew P. King;Christian Buerger;Christian Buerger;Charalampos Tsoumpas;Charalampos Tsoumpas;Paul K. Marsden;Paul K. Marsden

  • Alignment of sparse freehand 3-D ultrasound with preoperative images of the liver using models of respiratory motion and deformation

    J.M. Blackall;G.P. Penney;A.P. King;D.J. Hawkes

  • Hierarchical adaptive local affine registration for fast and robust respiratory motion estimation.

    Christian Buerger;Tobias Schaeffter;Andrew P. King

  • Fast generation of 4D PET-MR data from real dynamic MR acquisitions

    Charalampos Tsoumpas;Christian Buerger;Andrew King;P. Mollet

  • A subject-specific technique for respiratory motion correction in image-guided cardiac catheterisation procedures

    Andrew P. King;Andrew P. King;Redha Boubertakh;Redha Boubertakh;Kawal S. Rhode;Kawal S. Rhode;YingLiang Ma;YingLiang Ma

  • Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning.

    Ilkay Öksüz;Bram Ruijsink;Esther Puyol-Antón;James R. Clough

  • Estimation of passive and active properties in the human heart using 3D tagged MRI

    Liya Asner;Myrianthi Hadjicharalambous;Radomir Chabiniok;Devis Peresutti

  • Simultaneous PET-MR acquisition and MR-derived motion fields for correction of non-rigid motion in PET.

    Charalampos Tsoumpas;Jane E. Mackewn;Philip Halsted;Andrew P. King

  • Fairness in Cardiac MR Image Analysis: An Investigation of Bias Due to Data Imbalance in Deep Learning Based Segmentation.

    Esther Puyol-Antón;Bram Ruijsink;Stefan K. Piechnik;Stefan Neubauer

  • A system for microscope-assisted guided interventions.

    A.P. King;P.J. Edwards;C.R. Maurer;D.A. de Cunha

  • Deep Learning-Based Detection and Correction of Cardiac MR Motion Artefacts During Reconstruction for High-Quality Segmentation

    Ilkay Oksuz;James R. Clough;Bram Ruijsink;Esther Puyol Anton

  • Nonrigid Motion Modeling of the Liver From 3-D Undersampled Self-Gated Golden-Radial Phase Encoded MRI

    C. Buerger;R. E. Clough;A. P. King;T. Schaeffter

  • A Stochastic Iterative Closest Point Algorithm (stochastICP)

    Graeme P. Penney;Philip J. Edwards;Andrew P. King;Jane M. Blackall

  • Stereo Augmented Reality in the Surgical Microscope

    A. P. King;P. J. Edwards;C. R. Maurer;D. A. de Cunha

  • Information Processing in Medical Imaging (IPMI)

    Christian Baumgartner;Christoph Kolbitsch;Jamie McClelland;Daniel Rueckert

  • Design and Evaluation of a System for Microscope-Assisted Guided Interventions (MAGI)

    Philip J. Edwards;Andrew P. King;Calvin R. Maurer;Darryl A. de Cunha

Frequent Co-Authors

Reza Razavi
Reza Razavi King's College London
Daniel Rueckert
Daniel Rueckert Technical University of Munich
Julia A. Schnabel
Julia A. Schnabel King's College London
Kawal Rhode
Kawal Rhode King's College London
Graeme P. Penney
Graeme P. Penney King's College London
Claudia Prieto
Claudia Prieto Pontificia Universidad Católica de Chile
David J. Hawkes
David J. Hawkes University College London
Wenjia Bai
Wenjia Bai Imperial College London
Paul Aljabar
Paul Aljabar King's College London
Derek L. G. Hill
Derek L. G. Hill Panoramic Digital Health

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