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Konstantinos Kamnitsas

Konstantinos Kamnitsas

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Rising Stars
2025

D-Index & Metrics

Rising Stars

D-Index
39
Citations
14168
World Ranking
678
National Ranking
37

Computer Science

D-Index
31
Citations
10644
World Ranking
13343
National Ranking
849

Research.com Recognitions

  • 2025 - Research.com Rising Stars Award

Overview

Konstantinos Kamnitsas is affiliated with the University of Oxford in the United Kingdom. Their research spans interdisciplinary fields within computer science and medicine, with a focus on artificial intelligence and medical imaging.

Their primary fields of study include:

  • Computer Science
  • Medicine

Within these domains, Kamnitsas has contributed to several subfields such as:

  • Artificial Intelligence
  • Radiology, Nuclear Medicine and Imaging
  • Computer Vision and Pattern Recognition
  • Neurology
  • Biomedical Engineering

Their main research topics encompass:

  • Domain Adaptation and Few-Shot Learning
  • Advanced Neural Network Applications
  • COVID-19 diagnosis using AI
  • Radiomics and Machine Learning in Medical Imaging
  • Medical Image Segmentation Techniques
  • Anomaly Detection Techniques and Applications
  • Brain Tumor Detection and Classification

Kamnitsas has authored multiple recent papers published in reputable venues. Selected works include:

  • "Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI," 2022, Nature Medicine
  • "Multiclass semantic segmentation and quantification of traumatic brain injury lesions on head CT using deep learning: an algorithm development and multicentre validation study," 2020, The Lancet Digital Health
  • "Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty," 2020, arXiv (Cornell University)
  • "A Review of the Metrics Used to Assess Auto-Contouring Systems in Radiotherapy," 2023, Clinical Oncology
  • "Relationship of admission blood proteomic biomarkers levels to lesion type and lesion burden in traumatic brain injury: A CENTER-TBI study," 2021, EBioMedicine

Frequent co-authors collaborating with Kamnitsas include:

  • Ben Glocker
  • F. Wagner
  • J. Alison Noble
  • Pramit Saha
  • David Menon

They have published extensively in venues such as:

  • arXiv (Cornell University)
  • IEEE Transactions on Medical Imaging
  • Nature Medicine
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • The Lancet Digital Health

In the area of book publications, Kamnitsas has contributed to works published by Springer Science+Business Media, including titles focused on domain adaptation, representation transfer, and healthcare AI across several years from 2020 to 2023.

Best Publications

  • Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation

    Konstantinos Kamnitsas;Christian Ledig;Virginia F.J. Newcombe;Joanna P. Simpson

  • Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

    Spyridon Bakas;Mauricio Reyes;Andras Jakab;Stefan Bauer

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

    Ozan Oktay;Enzo Ferrante;Konstantinos Kamnitsas;Mattias Heinrich

  • ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI

    Oskar Maier;Bjoern H. Menze;Janina von der Gablentz;Levin Häni

  • Unsupervised domain adaptation in brain lesion segmentation with adversarial networks

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

  • Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation

    Konstantinos Kamnitsas;Wenjia Bai;Enzo Ferrante;Steven G. McDonagh

  • Domain Generalization via Model-Agnostic Learning of Semantic Features

    Qi Dou;Daniel Coelho de Castro;Konstantinos Kamnitsas;Ben Glocker

  • 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

  • DeepMedic for Brain Tumor Segmentation

    Konstantinos Kamnitsas;Konstantinos Kamnitsas;Enzo Ferrante;Sarah Parisot;Christian Ledig

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

    Ozan Oktay;Enzo Ferrante;Konstantinos Kamnitsas;Mattias Heinrich

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

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

  • Evaluating reinforcement learning agents for anatomical landmark detection.

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

  • Reverse Classification Accuracy: Predicting Segmentation Performance in the Absence of Ground Truth

    Vanya V. Valindria;Ioannis Lavdas;Wenjia Bai;Konstantinos Kamnitsas

  • Multiclass semantic segmentation and quantification of traumatic brain injury lesions on head CT using deep learning: an algorithm development and multicentre validation study

    Miguel Monteiro;Virginia F J Newcombe;Francois Mathieu;Krishma Adatia

  • Multi-scale 3D convolutional neural networks for lesion segmentation in brain MRI

    K Kamnitsas;L Chen;C Ledig;D Rueckert

  • Autofocus Layer for Semantic Segmentation

    Yao Qin;Konstantinos Kamnitsas;Siddharth Ancha;Jay Nanavati

  • Overfitting of Neural Nets Under Class Imbalance: Analysis and Improvements for Segmentation

    Zeju Li;Konstantinos Kamnitsas;Ben Glocker

  • Data Efficient Unsupervised Domain Adaptation For Cross-modality Image Segmentation

    Cheng Ouyang;Konstantinos Kamnitsas;Carlo Biffi;Jinming Duan

  • Unsupervised Lesion Detection in Brain CT using Bayesian Convolutional Autoencoders

    Nick Pawlowski;Matthew C.H. Lee;Martin Rajchl;Steven McDonagh

  • 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

  • Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty

    Miguel Monteiro;Loic Le Folgoc;Daniel Coelho de Castro;Nick Pawlowski

Frequent Co-Authors

Ben Glocker
Ben Glocker Imperial College London
Daniel Rueckert
Daniel Rueckert Technical University of Munich
Bernhard Kainz
Bernhard Kainz Imperial College London
Wenjia Bai
Wenjia Bai Imperial College London
Ozan Oktay
Ozan Oktay Imperial College London
Christian Ledig
Christian Ledig University of Bamberg
Antonio Criminisi
Antonio Criminisi Microsoft (United States)
Martin Rajchl
Martin Rajchl Imperial College London
Aditya V. Nori
Aditya V. Nori Microsoft (United States)
Joseph V. Hajnal
Joseph V. Hajnal King's College London

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