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Carola-Bibiane Schönlieb

Carola-Bibiane Schönlieb

University of Cambridge
United Kingdom

D-Index & Metrics

Computer Science

D-Index
49
Citations
10634
World Ranking
5850
National Ranking
350

Carola-Bibiane Schönlieb 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 Carola-Bibiane Schönlieb 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: 356 publications — 82nd percentile

82% of scientists in this discipline score the same or lower.

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

Carola-Bibiane Schönlieb 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 Carola-Bibiane Schönlieb 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: 49 D-Index — 60th percentile

60% of scientists in this discipline score the same or lower.

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

Research.com Recognitions

  • Member of the Norwegian Academy of Science and Letters Mathematics
  • Member of the Norwegian Academy of Science and Letters Mathematics
  • Member of the Norwegian Academy of Science and Letters Mathematics
  • Member of the Norwegian Academy of Science and Letters Mathematics

Overview

Carola-Bibiane Schönlieb is affiliated with the University of Cambridge in the United Kingdom. Their research spans several interdisciplinary fields with a primary focus on computer science and medicine.

The main fields of study for Schönlieb include:

  • Computer Science
  • Medicine

Their work covers multiple subfields, notably:

  • Computer Vision and Pattern Recognition
  • Radiology, Nuclear Medicine and Imaging
  • Artificial Intelligence
  • Computational Mechanics
  • Biomedical Engineering

Schönlieb's research topics include:

  • Radiomics and Machine Learning in Medical Imaging
  • Sparse and Compressive Sensing Techniques
  • Medical Imaging Techniques and Applications
  • Medical Image Segmentation Techniques
  • AI in cancer detection
  • Generative Adversarial Networks and Image Synthesis
  • Advanced X-ray and CT Imaging

Frequent co-authors working with Schönlieb are:

  • Angelica I. Aviles-Rivero
  • Evis Sala
  • Michael Roberts
  • Subhadip Mukherjee
  • James H.F. Rudd

Schönlieb has published extensively in various venues, with high activity at:

  • arXiv (Cornell University)
  • SIAM Journal on Imaging Sciences
  • Zenodo (CERN European Organization for Nuclear Research)
  • Nature Machine Intelligence
  • Inverse Problems

Selected recent papers include:

  • "Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans" (2020), Research Portal (King's College London)
  • "Unified Focal loss: Generalising Dice and cross entropy-based losses to handle class imbalanced medical image segmentation" (2021), Computerized Medical Imaging and Graphics
  • "A deep-learning pipeline for the diagnosis and discrimination of viral, non-viral and COVID-19 pneumonia from chest X-ray images" (2021), Nature Biomedical Engineering
  • "Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions" (2022), Information Fusion
  • "Can physics-informed neural networks beat the finite element method?" (2024), IMA Journal of Applied Mathematics

Schönlieb has contributed to book publications, including a forthcoming title:

  • "The Art of Inpainting" (2025), Cambridge University Press

The scientist has been recognized as a member of the Norwegian Academy of Science and Letters in the field of mathematics.

Best Publications

  • Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans

    Michael Roberts;Michael Roberts;Derek Driggs;Matthew Thorpe;Julian D. Gilbey

  • Solving inverse problems using data-driven models

    Simon R. Arridge;Peter Maass;Ozan Öktem;Carola-Bibiane Schönlieb

  • Unified Focal loss: Generalising Dice and cross entropy-based losses to handle class imbalanced medical image segmentation

    Michael Yeung;Evis Sala;Carola-Bibiane Schönlieb;Leonardo Rundo

  • Learning to Diversify Deep Belief Networks for Hyperspectral Image Classification

    Ping Zhong;Zhiqiang Gong;Shutao Li;Carola-Bibiane Schonlieb

  • A Combined First and Second Order Variational Approach for Image Reconstruction

    K. Papafitsoros;C. B. Schönlieb

  • Cahn-Hilliard Inpainting and a Generalization for Grayvalue Images

    Martin Burger;Lin He;Carola-Bibiane Schönlieb

  • A deep-learning pipeline for the diagnosis and discrimination of viral, non-viral and COVID-19 pneumonia from chest X-ray images.

    Guangyu Wang;Xiaohong Liu;Jun Shen;Chengdi Wang

  • Stochastic Primal-Dual Hybrid Gradient Algorithm with Arbitrary Sampling and Imaging Applications

    Antonin Chambolle;Matthias J. Ehrhardt;Peter Richtárik;Peter Richtárik;Carola-Bibiane Schönlieb

  • Adversarial Regularizers in Inverse Problems

    Sebastian Lunz;Ozan Öktem;Carola-Bibiane Schönlieb

  • AI-Based Reconstruction for Fast MRI—A Systematic Review and Meta-Analysis

    Unknown

  • Superpixel Contracted Graph-Based Learning for Hyperspectral Image Classification

    Philip Sellars;Angelica I. Aviles-Rivero;Carola-Bibiane Schonlieb

  • Focus U-Net: A novel dual attention-gated CNN for polyp segmentation during colonoscopy.

    Michael Yeung;Evis Sala;Carola-Bibiane Schönlieb;Leonardo Rundo

  • Variational Depth From Focus Reconstruction

    Michael Moeller;Martin Benning;Carola Schonlieb;Daniel Cremers

  • Bilevel Parameter Learning for Higher-Order Total Variation Regularisation Models

    J. C. Reyes;C. B. Schönlieb;T. Valkonen

  • Image denoising: Learning the noise model via nonsmooth PDE-constrained optimization

    Juan Carlos De los Reyes;Carola-Bibiane Schönlieb

  • Imaging with Kantorovich--Rubinstein Discrepancy

    Jan Lellmann;Dirk A. Lorenz;Carola-Bibiane Schönlieb;Tuomo Valkonen

  • On the Connection Between Adversarial Robustness and Saliency Map Interpretability

    Christian Etmann;Sebastian Lunz;Peter Maass;Carola-Bibiane Schönlieb

  • Bilevel parameter learning for higher-order total variation regularisation models

    J.C. De los Reyes;C.-B. Schönlieb;T. Valkonen

  • Liquid phase blending of metal-organic frameworks.

    Louis Longley;Sean Michael Collins;Chao Zhou;Glen J Smales

  • Partial differential equation methods for image inpainting

    Carola-Bibiane Schönlieb

  • Individual Tree Species Classification From Airborne Multisensor Imagery Using Robust PCA

    Juheon Lee;Xiaohao Cai;Jan Lellmann;Michele Dalponte

  • Deep learning as optimal control problems: Models and numerical methods

    Martin Benning;Elena Celledoni;Matthias J. Ehrhardt;Brynjulf Owren

  • Bilevel approaches for learning of variational imaging models.

    Luca Calatroni;Cao Chung;Juan Carlos de los Reyes;Carola-Bibiane Schönlieb

Frequent Co-Authors

Martin Burger
Martin Burger University of Erlangen-Nuremberg
Michael S. Roberts
Michael S. Roberts University of Queensland
David A. Coomes
David A. Coomes University of Cambridge
Paul A. Midgley
Paul A. Midgley University of Cambridge
Massimo Fornasier
Massimo Fornasier Technical University of Munich
Peter Richtárik
Peter Richtárik King Abdullah University of Science and Technology
Simon R. Arridge
Simon R. Arridge University College London
Yuanzheng Yue
Yuanzheng Yue Aalborg University
Peter Maass
Peter Maass University of Bremen
Robby T. Tan
Robby T. Tan National University of Singapore

External Links

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Best Scientists Citing Carola-Bibiane Schönlieb

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