World's Best Scientists 2026 revealed!
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Computer Science
UK
2025

D-Index & Metrics

Computer Science

D-Index
85
Citations
42124
World Ranking
788
National Ranking
427

Research.com Recognitions

  • 2025 - Research.com Computer Science in United Kingdom Leader Award
  • 2023 - Research.com Computer Science in United Kingdom Leader Award
  • 2022 - Research.com Computer Science in United Kingdom Leader Award

Overview

Antonio Criminisi is affiliated with Microsoft in the United States and works primarily in the fields of Computer Science and Medicine. Their research spans multiple subfields, with a focus on Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics, and Artificial Intelligence.

The scientist's key research topics include Advanced Neuroimaging Techniques and Applications, Model Reduction and Neural Networks, Advanced MRI Techniques and Applications, Advanced Neural Network Applications, Domain Adaptation and Few-Shot Learning, and Multimodal Machine Learning Applications.

Antonio Criminisi has contributed to publications in well-known venues. Notable recent papers include:

  • "Uncertainty modelling in deep learning for safer neuroimage enhancement: Demonstration in diffusion MRI" (2020, NeuroImage)
  • "Sequential Ensembling for Semantic Segmentation" (2022, arXiv [Cornell University])

The scientist has collaborated with several frequent co-authors, including:

  • Ryutaro Tanno
  • Daniel E. Worrall
  • Enrico Kaden
  • Aurobrata Ghosh
  • Francesco Grussu

Antonio Criminisi's work in deep learning and machine learning methods addresses the challenges of medical imaging analysis, emphasizing safer enhancement techniques in neuroimaging and semantic segmentation approaches. This interdisciplinary focus bridges advanced computational techniques with clinical imaging applications.

Their research contributes to the ongoing development of models that integrate complex neural networks with practical applications in medicine, particularly through the use of multimodal data and domain adaptation strategies. This approach aligns with current trends toward leveraging artificial intelligence to improve diagnostic accuracy and imaging quality in healthcare settings.

Best Publications

  • The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

    Bjoern H. Menze;Andras Jakab;Stefan Bauer;Jayashree Kalpathy-Cramer

  • Region filling and object removal by exemplar-based image inpainting

    A. Criminisi;P. Perez;K. Toyama

  • TextonBoost : joint appearance, shape and context modeling for multi-class object recognition and segmentation

    Jamie Shotton;John Winn;Carsten Rother;Antonio Criminisi

  • TextonBoost for Image Understanding: Multi-Class Object Recognition and Segmentation by Jointly Modeling Texture, Layout, and Context

    Jamie Shotton;John Winn;Carsten Rother;Antonio Criminisi

  • Object removal by exemplar-based inpainting

    A. Criminisi;P. Perez;K. Toyama

  • Object categorization by learned universal visual dictionary

    J. Winn;A. Criminisi;T. Minka

  • Single View Metrology

    A. Criminisi;I. Reid;A. Zisserman

  • Epitomic location recognition

    Kai Ni;A. Kannan;A. Criminisi;J. Winn

  • Decision Forests: A Unified Framework for Classification, Regression, Density Estimation, Manifold, Learning and Semi-supervised Learning

    Antonio Criminisi;Jamie Shotton;Ender Konukoglu

  • Scene Coordinate Regression Forests for Camera Relocalization in RGB-D Images

    Jamie Shotton;Ben Glocker;Christopher Zach;Shahram Izadi

  • Decision Forests for Computer Vision and Medical Image Analysis

    A. Criminisi;J. Shotton

  • Efficient Human Pose Estimation from Single Depth Images

    Jamie Shotton;Ross Girshick;Andrew Fitzgibbon;Toby Sharp

  • A plane measuring device

    Antonio Criminisi;Ian D. Reid;Andrew Zisserman

  • Deep Neural Decision Forests

    Peter Kontschieder;Madalina Fiterau;Antonio Criminisi;Samuel Rota Bulo

  • Efficient regression of general-activity human poses from depth images

    Ross Girshick;Jamie Shotton;Pushmeet Kohli;Antonio Criminisi

  • Harvesting Image Databases from the Web

    F Schroff;A Criminisi;A Zisserman

  • Unsupervised domain adaptation in brain lesion segmentation with adversarial networks

    Konstantinos Kamnitsas;Konstantinos Kamnitsas;Christian F. Baumgartner;Christian Ledig;Virginia F. J. Newcombe

  • Geodesic star convexity for interactive image segmentation

    Varun Gulshan;Carsten Rother;Antonio Criminisi;Andrew Blake

  • Creating Architectural Models from Images

    David Liebowitz;Antonio Criminisi;Andrew Zisserman

  • Regression forests for efficient anatomy detection and localization in CT studies

    Antonio Criminisi;Jamie Shotton;Duncan Robertson;Ender Konukoglu

  • Efficient Regression of General-Activity Human Poses from Depth Images: Supplementary Material

    Ross Girshick;Jamie Shotton;Pushmeet Kohli;Antonio Criminisi

Frequent Co-Authors

Jamie Shotton
Jamie Shotton Microsoft (United States)
Ben Glocker
Ben Glocker Imperial College London
Andrew Blake
Andrew Blake University of Cambridge
Nicholas Ayache
Nicholas Ayache French Institute for Research in Computer Science and Automation - INRIA
John Winn
John Winn Microsoft (United States)
Carsten Rother
Carsten Rother Heidelberg University
Abigail Sellen
Abigail Sellen Microsoft (United States)
Andrew Zisserman
Andrew Zisserman University of Oxford
Aditya V. Nori
Aditya V. Nori Microsoft (United States)

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