World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
33
Citations
12278
World Ranking
12361
National Ranking
789

Overview

Ozan Oktay is affiliated with Imperial College London in the United Kingdom. Their research primarily intersects the fields of Computer Science and Medicine, with a significant focus on Artificial Intelligence and its applications within healthcare and medical imaging.

Their scholarly output includes contributions across several specialized subfields, notably Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition, Health Informatics, and Oncology. This diverse range reflects a multidisciplinary approach to medical and computational challenges.

The main topics of Ozan Oktay's work cover a variety of areas within medical imaging and artificial intelligence, such as Radiomics and Machine Learning in Medical Imaging, Topic Modeling, Artificial Intelligence in Healthcare and Education, Natural Language Processing Techniques, Multimodal Machine Learning Applications, COVID-19 diagnosis using AI, and Explainable Artificial Intelligence (XAI).

Some of their recent publications include:

  • "Active label cleaning for improved dataset quality under resource constraints," 2022, Nature Communications
  • "Evaluation of Deep Learning to Augment Image-Guided Radiotherapy for Head and Neck and Prostate Cancers," 2020, JAMA Network Open
  • "A causal perspective on dataset bias in machine learning for medical imaging," 2024, Nature Machine Intelligence
  • "Large-scale Quality Control of Cardiac Imaging in Population Studies: Application to UK Biobank," 2020, Scientific Reports
  • "Exploring scalable medical image encoders beyond text supervision," 2025, Nature Machine Intelligence

Frequent co-authors collaborating with Ozan Oktay include:

  • Javier Alvarez-Valle
  • Daniel C. Castro
  • Anton Schwaighofer
  • Shruthi Bannur
  • Fernando Pérez-García

Ozan Oktay's publications are often found in both preprint and peer-reviewed venues. They have contributed extensively to arXiv (Cornell University) and have multiple publications in Nature Communications and Nature Machine Intelligence. Other publication venues include JAMA Network Open and Scientific Reports.

Best Publications

  • Attention U-Net: Learning Where to Look for the Pancreas

    Ozan Oktay;Jo Schlemper;Loïc Le Folgoc;Matthew C. H. Lee

  • Attention gated networks: Learning to leverage salient regions in medical images.

    Jo Schlemper;Ozan Oktay;Michiel Schaap;Mattias P. Heinrich

  • 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

  • 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

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

    Ozan Oktay;Enzo Ferrante;Konstantinos Kamnitsas;Mattias Heinrich

  • Making the Most of Text Semantics to Improve Biomedical Vision-Language Processing

    Unknown

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

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

  • White matter hyperintensity and stroke lesion segmentation and differentiation using convolutional neural networks.

    R. Guerrero;C. Qin;O. Oktay;C. Bowles

  • Evaluating reinforcement learning agents for anatomical landmark detection.

    Amir Alansary;Ozan Oktay;Yuanwei Li;Loic Le Folgoc

  • Recurrent Neural Networks for Aortic Image Sequence Segmentation with Sparse Annotations

    Wenjia Bai;Hideaki Suzuki;Chen Qin;Giacomo Tarroni

  • Learning to Exploit Temporal Structure for Biomedical Vision-Language Processing

    Unknown

  • Active label cleaning for improved dataset quality under resource constraints

    Unknown

  • Standardized Evaluation System for Left Ventricular Segmentation Algorithms in 3D Echocardiography

    Olivier Bernard;Johan G. Bosch;Brecht Heyde;Martino Alessandrini

  • Adversarial and Perceptual Refinement for Compressed Sensing MRI Reconstruction

    Maximilian Seitzer;Guang Yang;Jo Schlemper;Ozan Oktay

  • Attention-Gated Networks for Improving Ultrasound Scan Plane Detection

    Jo Schlemper;Ozan Oktay;Liang Chen;Jacqueline Matthew

  • Attention-Gated Networks for Improving Ultrasound Scan Plane Detection

    Jo Schlemper;Ozan Oktay;Liang Chen;Jacqueline Matthew

  • Stratified Decision Forests for Accurate Anatomical Landmark Localization in Cardiac Images

    Ozan Oktay;Wenjia Bai;Ricardo Guerrero;Martin Rajchl

  • Automated quality control in image segmentation: application to the UK Biobank cardiac MR imaging study

    Robert Robinson;Vanya V. Valindria;Wenjia Bai;Ozan Oktay

  • Learning interpretable anatomical features through deep generative models: Application to cardiac remodeling

    Carlo Biffi;Ozan Oktay;Giacomo Tarroni;Wenjia Bai

  • Image-and-spatial transformer networks for structure-guided image registration

    Matthew C. H. Lee;Ozan Oktay;Andreas Schuh;Michiel Schaap

  • OBELISK-Net: Fewer layers to solve 3D multi-organ segmentation with sparse deformable convolutions.

    Mattias P. Heinrich;Ozan Oktay;Nassim Bouteldja

  • Automated quality control in image segmentation: application to the UK Biobank cardiovascular magnetic resonance imaging study

    Robert Robinson;Vanya V. Valindria;Wenjia Bai;Ozan Oktay

Frequent Co-Authors

Daniel Rueckert
Daniel Rueckert Technical University of Munich
Wenjia Bai
Wenjia Bai Imperial College London
Ben Glocker
Ben Glocker Imperial College London
Bernhard Kainz
Bernhard Kainz Imperial College London
Mattias P. Heinrich
Mattias P. Heinrich University of Lübeck
Martin Rajchl
Martin Rajchl Imperial College London
Konstantinos Kamnitsas
Konstantinos Kamnitsas University of Oxford
Paul M. Matthews
Paul M. Matthews Imperial College London
Joseph V. Hajnal
Joseph V. Hajnal King's College London
Aditya V. Nori
Aditya V. Nori Microsoft (United States)

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