World's Best Scientists 2026 revealed!
Carola-Bibiane Schönlieb

Carola-Bibiane Schönlieb

D-Index & Metrics

Computer Science

D-Index
49
Citations
10634
World Ranking
5849
National Ranking
352

Research.com Recognitions

  • 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
Paul A. Midgley
Paul A. Midgley University of Cambridge
Massimo Fornasier
Massimo Fornasier Technical University of Munich
Simon R. Arridge
Simon R. Arridge University College London
Peter Richtárik
Peter Richtárik King Abdullah University of Science and Technology
Yuanzheng Yue
Yuanzheng Yue Aalborg University
Peter Maass
Peter Maass University of Bremen
Daniel Cremers
Daniel Cremers Technical University of Munich
Hua Huang
Hua Huang University of California, Merced
Thomas D. Bennett
Thomas D. Bennett University of Cambridge

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