World's Best Scientists 2026 revealed!
Award Badge
Computer Science
Netherlands
2025

D-Index & Metrics

Computer Science

D-Index
52
Citations
14842
World Ranking
4993
National Ranking
72

Research.com Recognitions

  • 2025 - Research.com Computer Science in Netherlands Leader Award
  • 2022 - Research.com Computer Science in Netherlands Leader Award

Overview

Ivana Išgum is affiliated with the University of Amsterdam in the Netherlands and has a research focus largely centered on medical imaging, particularly in cardiology and radiology. Their work integrates advanced imaging techniques and machine learning applications to enhance diagnostic capabilities and risk stratification in cardiovascular diseases.

The primary fields of study covered by Ivana Išgum include Medicine, with a strong specialization in Radiology, Nuclear Medicine and Imaging, Cardiology and Cardiovascular Medicine, Biomedical Engineering, Surgery, and Pulmonary and Respiratory Medicine. These subfields reflect a multidisciplinary approach combining clinical and technological perspectives.

The main research topics addressed by Ivana Išgum encompass:

  • Cardiac Imaging and Diagnostics
  • Advanced X-ray and CT Imaging
  • Radiomics and Machine Learning in Medical Imaging
  • Coronary Interventions and Diagnostics
  • Medical Imaging Techniques and Applications
  • Medical Image Segmentation Techniques
  • Cardiovascular Function and Risk Factors

Ivana Išgum has contributed to multiple recent papers including:

  • Deep Learning for Automatic Calcium Scoring in CT: Validation Using Multiple Cardiac CT and Chest CT Protocols (2020), published in Radiology
  • Automated Assessment of COVID-19 Reporting and Data System and Chest CT Severity Scores in Patients Suspected of Having COVID-19 Using Artificial Intelligence (2020), published in Radiology
  • Multi-ancestry genome-wide study identifies effector genes and druggable pathways for coronary artery calcification (2023), published in Nature Genetics
  • Artificial Intelligence in Cardiovascular Imaging for Risk Stratification in Coronary Artery Disease (2021), published in Radiology Cardiothoracic Imaging
  • Clinical quantitative coronary artery stenosis and coronary atherosclerosis imaging: a Consensus Statement from the Quantitative Cardiovascular Imaging Study Group (2023), published in Nature Reviews Cardiology

Frequent collaborators include Bob D. de Vos, Sanne G. M. van Velzen, R. Nils Planken, Tim Leiner, and Bram van Ginneken, reflecting a network of co-authorship in cardiovascular imaging and biomedical research.

Ivana Išgum's publications are predominantly found in venues such as arXiv (Cornell University), Scientific Reports, Journal of Medical Imaging, Medical Image Analysis, and Journal of the American College of Cardiology. This distribution indicates engagement with both preprint repositories and peer-reviewed journals specializing in medical imaging and cardiovascular research.

Best Publications

  • Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?

    Olivier Bernard;Alain Lalande;Clement Zotti;Frederick Cervenansky

  • Generative Adversarial Networks for Noise Reduction in Low-Dose CT

    Jelmer M. Wolterink;Tim Leiner;Max A. Viergever;Ivana Isgum

  • Automatic Segmentation of MR Brain Images With a Convolutional Neural Network

    Pim Moeskops;Max A. Viergever;Adrienne M. Mendrik;Linda S. de Vries

  • A deep learning framework for unsupervised affine and deformable image registration

    Bob D. de Vos;Floris F. Berendsen;Max A. Viergever;Hessam Sokooti

  • Deep MR to CT synthesis using unpaired data

    Jelmer M. Wolterink;Anna M. Dinkla;Mark H. F. Savenije;Peter R. Seevinck

  • End-to-End Unsupervised Deformable Image Registration with a Convolutional Neural Network

    Bob D. de Vos;Floris F. Berendsen;Max A. Viergever;Marius Staring

  • Multi-Atlas-Based Segmentation With Local Decision Fusion—Application to Cardiac and Aortic Segmentation in CT Scans

    I. Isgum;M. Staring;A. Rutten;M. Prokop

  • Nonrigid image registration using multi-scale 3D convolutional neural networks

    Hessam Sokooti;Bob D. de Vos;Floris F. Berendsen;Boudewijn P. F. Lelieveldt;Boudewijn P. F. Lelieveldt

  • State-of-the-Art Deep Learning in Cardiovascular Image Analysis

    Geert Litjens;Francesco Ciompi;Jelmer M. Wolterink;Bob D. de Vos

  • Deep learning for multi-task medical image segmentation in multiple modalities

    Pim Moeskops;Pim Moeskops;Jelmer M. Wolterink;Bas H. M. van der Velden;Kenneth G. A. Gilhuijs

  • A Recurrent CNN for Automatic Detection and Classification of Coronary Artery Plaque and Stenosis in Coronary CT Angiography

    Majd Zreik;Robbert W. van Hamersvelt;Jelmer M. Wolterink;Tim Leiner

  • Automatic coronary artery calcium scoring in cardiac CT angiography using paired convolutional neural networks.

    Jelmer M. Wolterink;Tim Leiner;Bob D. de Vos;Robbert W. van Hamersvelt

  • Machine learning in cardiovascular magnetic resonance: basic concepts and applications

    Tim Leiner;Daniel Rueckert;Avan Suinesiaputra;Bettina Baeßler

  • Automatic Calcium Scoring in Low-Dose Chest CT Using Deep Neural Networks With Dilated Convolutions

    Nikolas Lessmann;Bram van Ginneken;Majd Zreik;Pim A. de Jong

  • Iterative fully convolutional neural networks for automatic vertebra segmentation and identification

    Nikolas Lessmann;Bram van Ginneken;Pim A. de Jong;Ivana Išgum

  • Deep learning analysis of the myocardium in coronary CT angiography for identification of patients with functionally significant coronary artery stenosis

    Majd Zreik;Nikolas Lessmann;Robbert W. van Hamersvelt;Jelmer M. Wolterink

  • Adaptive local multi-atlas segmentation : Application to the heart and the caudate nucleus

    Eva M. van Rikxoort;Ivana Isgum;Yulia Arzhaeva;Marius Staring

  • Coronary artery centerline extraction in cardiac CT angiography using a CNN-based orientation classifier

    Jelmer M. Wolterink;Robbert W. van Hamersvelt;Max A. Viergever;Tim Leiner

  • Automatic Coronary Calcium Scoring in Low-Dose Chest Computed Tomography

    I. Isgum;M. Prokop;M. Niemeijer;M. A. Viergever

  • MR-Only Brain Radiation Therapy: Dosimetric Evaluation of Synthetic CTs Generated by a Dilated Convolutional Neural Network

    Anna M. Dinkla;Jelmer M. Wolterink;Matteo Maspero;Mark H.F. Savenije

  • Automated Assessment of COVID-19 Reporting and Data System and Chest CT Severity Scores in Patients Suspected of Having COVID-19 Using Artificial Intelligence

    Nikolas Lessmann;Clara I. Sánchez;Ludo Beenen;Luuk H. Boulogne

Frequent Co-Authors

Max A. Viergever
Max A. Viergever Utrecht University
Bram van Ginneken
Bram van Ginneken Radboud University
Marius Staring
Marius Staring Leiden University Medical Center
Jörg Sander
Jörg Sander University of Alberta
Josien P. W. Pluim
Josien P. W. Pluim Eindhoven University of Technology
Clara I. Sánchez
Clara I. Sánchez University of Amsterdam
Paul A.M. Smeets
Paul A.M. Smeets Wageningen University & Research
Frank van Bel
Frank van Bel Utrecht University
Jessica Dubois
Jessica Dubois Université Paris Cité
Paul I. W. de Bakker
Paul I. W. de Bakker Vertex Pharmaceuticals (United Kingdom)

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Exploring online education can open new doors for those interested in computer science and related fields. Many online graduate schools with low gpa requirements make it easier for students with diverse academic backgrounds to get started, providing greater flexibility and access.

If you’re looking to fast-track your career, consider accelerated options such as a 2-year computer science degree online. These programs help you gain valuable skills in a shorter timeframe, making it easier to enter the tech workforce quickly.

Computer science isn’t the only pathway—environmental science and engineering are also growing fields. There are many jobs for environmental science majors that bridge technology, sustainability, and research. Additionally, pursuing environmental engineering degrees online can prepare you for impactful roles in addressing critical global challenges.

Whether you’re interested in computer science or related disciplines, flexible online degree pathways can help you reach your career goals without geographical limits.

Best Scientists Citing Ivana Išgum

Trending Scientists

Recently Published Articles