The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Antonio Criminisi sits on this spectrum.
This scientist: 262 publications — 65th percentile
65% of scientists in this discipline score the same or lower.
The last bar groups every scientist with 991 publications or more.
The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Antonio Criminisi sits on this spectrum.
This scientist: 85 D-Index — 94th percentile
94% of scientists in this discipline score the same or lower.
The last bar groups every scientist with 131 D-Index or more.
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:
The scientist has collaborated with several frequent co-authors, including:
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.
Bjoern H. Menze;Andras Jakab;Stefan Bauer;Jayashree Kalpathy-Cramer
A. Criminisi;P. Perez;K. Toyama
Jamie Shotton;John Winn;Carsten Rother;Antonio Criminisi
Jamie Shotton;John Winn;Carsten Rother;Antonio Criminisi
A. Criminisi;P. Perez;K. Toyama
J. Winn;A. Criminisi;T. Minka
A. Criminisi;I. Reid;A. Zisserman
Kai Ni;A. Kannan;A. Criminisi;J. Winn
Antonio Criminisi;Jamie Shotton;Ender Konukoglu
Jamie Shotton;Ben Glocker;Christopher Zach;Shahram Izadi
A. Criminisi;J. Shotton
Jamie Shotton;Ross Girshick;Andrew Fitzgibbon;Toby Sharp
Antonio Criminisi;Ian D. Reid;Andrew Zisserman
Peter Kontschieder;Madalina Fiterau;Antonio Criminisi;Samuel Rota Bulo
Ross Girshick;Jamie Shotton;Pushmeet Kohli;Antonio Criminisi
F Schroff;A Criminisi;A Zisserman
Konstantinos Kamnitsas;Konstantinos Kamnitsas;Christian F. Baumgartner;Christian Ledig;Virginia F. J. Newcombe
Varun Gulshan;Carsten Rother;Antonio Criminisi;Andrew Blake
David Liebowitz;Antonio Criminisi;Andrew Zisserman
Antonio Criminisi;Jamie Shotton;Duncan Robertson;Ender Konukoglu
Ross Girshick;Jamie Shotton;Pushmeet Kohli;Antonio Criminisi
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