World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
41
Citations
15075
World Ranking
8585
National Ranking
520

Overview

Bernhard Kainz is affiliated with Imperial College London in the United Kingdom. Their research spans across medicine and computer science, with significant contributions in artificial intelligence, radiology, nuclear medicine and imaging, as well as computer vision and pattern recognition. Their work also covers pediatrics, perinatology, child health, and health informatics.

The scientist's research topics include fetal and pediatric neurological disorders, radiomics and machine learning in medical imaging, COVID-19 diagnosis using AI, domain adaptation and few-shot learning, artificial intelligence applications in healthcare and education, anomaly detection techniques and applications, and medical image segmentation techniques.

Recent notable publications by Bernhard Kainz include:

  • A survey on active learning and human-in-the-loop deep learning for medical image analysis, 2021, Medical Image Analysis
  • Metrics reloaded: recommendations for image analysis validation, 2024, Nature Methods
  • Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study, 2021, npj Digital Medicine
  • Understanding metric-related pitfalls in image analysis validation, 2024, Nature Methods
  • Geomstats: A Python Package for Riemannian Geometry in Machine Learning, 2020, arXiv (Cornell University)

Bernhard Kainz frequently collaborates with several researchers, including:

  • Daniel Rueckert
  • Thomas G. Day
  • Hadrien Reynaud
  • Jeremy Tan
  • Joseph V. Hajnal

Their research has been disseminated through various venues, with frequent publications in:

  • arXiv (Cornell University)
  • Lecture Notes in Computer Science
  • IEEE Transactions on Medical Imaging
  • npj Digital Medicine
  • Prenatal Diagnosis

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

  • A Survey on Active Learning and Human-in-the-Loop Deep Learning for Medical Image Analysis

    Unknown

  • 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

  • SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound

    Christian F. Baumgartner;Konstantinos Kamnitsas;Jacqueline Matthew;Tara P. Fletcher

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

    Ozan Oktay;Enzo Ferrante;Konstantinos Kamnitsas;Mattias Heinrich

  • Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study

    Qi Dou;Tiffany Y. So;Meirui Jiang;Quande Liu

  • Fast Volume Reconstruction From Motion Corrupted Stacks of 2D Slices

    Bernhard Kainz;Markus Steinberger;Wolfgang Wein;Maria Kuklisova-Murgasova

  • Natural Synthetic Anomalies for Self-supervised Anomaly Detection and Localization

    Unknown

  • Evaluating reinforcement learning agents for anatomical landmark detection.

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

  • Automated fetal brain segmentation from 2D MRI slices for motion correction.

    Kevin Keraudren;Maria Kuklisova-Murgasova;Vanessa Kyriakopoulou;Christina Malamateniou

  • 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

  • Real-Time Standard Scan Plane Detection and Localisation in Fetal Ultrasound Using Fully Convolutional Neural Networks

    Christian F. Baumgartner;Konstantinos Kamnitsas;Jacqueline Matthew;Sandra Smith

  • DLTK: State of the Art Reference Implementations for Deep Learning on Medical Images

    Nick Pawlowski;Sofia Ira Ktena;Matthew C. H. Lee;Bernhard Kainz

  • ScatterAlloc: Massively parallel dynamic memory allocation for the GPU

    Markus Steinberger;Michael Kenzel;Bernhard Kainz;Dieter Schmalstieg

  • OmniKinect: real-time dense volumetric data acquisition and applications

    Bernhard Kainz;Stefan Hauswiesner;Gerhard Reitmayr;Markus Steinberger

  • 3-D Reconstruction in Canonical Co-Ordinate Space From Arbitrarily Oriented 2-D Images

    Benjamin Hou;Bishesh Khanal;Amir Alansary;Steven McDonagh

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

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

  • Fast Fully Automatic Segmentation of the Human Placenta from Motion Corrupted MRI

    Amir Alansary;Konstantinos Kamnitsas;Alice Davidson;Rostislav Khlebnikov

  • Ray casting of multiple volumetric datasets with polyhedral boundaries on manycore GPUs

    Bernhard Kainz;Markus Grabner;Alexander Bornik;Stefan Hauswiesner

  • Geomstats: A Python Package for Riemannian Geometry in Machine Learning

    Nina Miolane;Johan Mathe;Claire Donnat;Mikael Jorda

Frequent Co-Authors

Daniel Rueckert
Daniel Rueckert Technical University of Munich
Dieter Schmalstieg
Dieter Schmalstieg University of Stuttgart
Ben Glocker
Ben Glocker Imperial College London
Ozan Oktay
Ozan Oktay Imperial College London
Joseph V. Hajnal
Joseph V. Hajnal King's College London
Konstantinos Kamnitsas
Konstantinos Kamnitsas University of Oxford
Wenjia Bai
Wenjia Bai Imperial College London
Martin Rajchl
Martin Rajchl Imperial College London
Mattias P. Heinrich
Mattias P. Heinrich University of Lübeck
Julia A. Schnabel
Julia A. Schnabel King's College London

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