World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
50
Citations
13281
World Ranking
5519
National Ranking
331

Wenjia Bai 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 Wenjia Bai 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: 156 publications — 29th percentile

29% of scientists in this discipline score the same or lower.

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

Wenjia Bai 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 Wenjia Bai 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: 50 D-Index — 62nd percentile

62% of scientists in this discipline score the same or lower.

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

Overview

Wenjia Bai is affiliated with Imperial College London in the United Kingdom and has contributed extensively to the fields of medicine and computer science. Their research primarily focuses on radiology, nuclear medicine and imaging, computer vision and pattern recognition, cardiology and cardiovascular medicine, molecular biology, and artificial intelligence. The scientist's work spans a wide range of topics including radiomics and machine learning in medical imaging, medical image segmentation techniques, advanced MRI techniques and applications, medical imaging techniques and applications, cardiovascular function and risk factors, cardiomyopathy and myosin studies, and cardiac imaging and diagnostics.

They have published frequently in prominent venues such as:

  • arXiv (Cornell University)
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Medical Image Analysis
  • IEEE Transactions on Medical Imaging
  • Nature Genetics

Among their recent papers are:

  • A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging, 2020, Medical Image Analysis
  • Shared genetic pathways contribute to risk of hypertrophic and dilated cardiomyopathies with opposite directions of effect, 2021, Nature Genetics
  • A population-based phenome-wide association study of cardiac and aortic structure and function, 2020, Nature Medicine
  • Clinical quantitative cardiac imaging for the assessment of myocardial ischaemia, 2020, Nature Reviews Cardiology
  • Genetic and functional insights into the fractal structure of the heart, 2020, Nature

Wenjia Bai collaborates regularly with several co-authors, including:

  • Daniel Rueckert
  • Declan P. O'Regan
  • Paul M. Matthews
  • James S. Ware
  • Antonio de Marvao

Their scholarly contributions reveal an interdisciplinary approach, integrating computational techniques and medical sciences, particularly in cardiovascular research and imaging. The combination of machine learning and advanced imaging techniques in their work aims to provide insights into cardiac function and disease mechanisms, reflecting active engagement across multiple scientific communities.

Best Publications

  • Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

    Spyridon Bakas;Mauricio Reyes;Andras Jakab;Stefan Bauer

  • Deep Learning for Cardiac Image Segmentation: A Review.

    Chen Chen;Chen Qin;Huaqi Qiu;Giacomo Tarroni;Giacomo Tarroni

  • Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation

    Ozan Oktay;Enzo Ferrante;Konstantinos Kamnitsas;Mattias Heinrich

  • Automated cardiovascular magnetic resonance image analysis with fully convolutional networks

    Wenjia Bai;Matthew Sinclair;Giacomo Tarroni;Ozan Oktay

  • Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation

    Konstantinos Kamnitsas;Wenjia Bai;Enzo Ferrante;Steven G. McDonagh

  • DeepCut: Object Segmentation From Bounding Box Annotations Using Convolutional Neural Networks

    Martin Rajchl;Matthew C. H. Lee;Ozan Oktay;Konstantinos Kamnitsas

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

    Wenjia Bai;Ozan Oktay;Matthew Sinclair;Hideaki Suzuki

  • A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging.

    Zhaohan Xiong;Qing Xia;Zhiqiang Hu;Ning Huang

  • Anatomically Constrained Neural Networks (ACNN): Application to Cardiac Image Enhancement and Segmentation

    Ozan Oktay;Enzo Ferrante;Konstantinos Kamnitsas;Mattias Heinrich

  • Cardiac image super-resolution with global correspondence using multi-atlas patchmatch.

    Wenzhe Shi;Jose Caballero;Christian Ledig;Xiahai Zhuang

  • A Probabilistic Patch-Based Label Fusion Model for Multi-Atlas Segmentation With Registration Refinement: Application to Cardiac MR Images

    Wenjia Bai;Wenzhe Shi;D. P. O'Regan;Tong Tong

  • Right ventricle segmentation from cardiac MRI: a collation study.

    Caroline Petitjean;Maria A. Zuluaga;Wenjia Bai;Jean Nicolas Dacher

  • Multi-input Cardiac Image Super-Resolution Using Convolutional Neural Networks

    Ozan Oktay;Wenjia Bai;Matthew C. H. Lee;Ricardo Guerrero

  • A population-based phenome-wide association study of cardiac and aortic structure and function

    Wenjia Bai;Hideaki Suzuki;Hideaki Suzuki;Jian Huang;Catherine Francis

  • Evaluation of current algorithms for segmentation of scar tissue from late Gadolinium enhancement cardiovascular magnetic resonance of the left atrium: an open-access grand challenge

    Rashed Karim;R. James Housden;Mayuragoban Balasubramaniam;Zhong Chen

  • Automatic 3D Bi-Ventricular Segmentation of Cardiac Images by a Shape-Refined Multi- Task Deep Learning Approach

    Jinming Duan;Ghalib Bello;Jo Schlemper;Wenjia Bai

  • 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

  • A bi-ventricular cardiac atlas built from 1000+ high resolution MR images of healthy subjects and an analysis of shape and motion

    Wenjia Bai;Wenzhe Shi;Antonio M. Simoes Monteiro de Marvao;Timothy J. W. Dawes

  • Self-supervised learning for cardiac MR image segmentation by anatomical position prediction

    Wenjia Bai;Chen Chen;Giacomo Tarroni;Jinming Duan

  • Multi-atlas segmentation with augmented features for cardiac MR images

    Wenjia Bai;Wenzhe Shi;Christian Ledig;Daniel Rueckert

  • Automatic 3D bi-ventricular segmentation of cardiac images by a shape-refined multi-task deep learning approach

    Jinming Duan;Ghalib Bello;Jo Schlemper;Wenjia Bai

Frequent Co-Authors

Daniel Rueckert
Daniel Rueckert Technical University of Munich
Ozan Oktay
Ozan Oktay Imperial College London
Ben Glocker
Ben Glocker Imperial College London
Steffen E. Petersen
Steffen E. Petersen Queen Mary University of London
Paul M. Matthews
Paul M. Matthews Imperial College London
Stuart A. Cook
Stuart A. Cook Duke NUS Graduate Medical School
Jinming Duan
Jinming Duan University of Manchester
Wenzhe Shi
Wenzhe Shi Twitter (United States)
Andrew P. King
Andrew P. King King's College London
Stefan K Piechnik
Stefan K Piechnik University of Oxford

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