Kamnitsas, Konstantinos

Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation

Kamnitsas, K., Ledig, C., Newcombe, V. F. J., Simpson, J. P., Kane, A. D., Menon, D. K., Rueckert, D., & Glocker, B. (2017) · Medical Image Analysis

Medical Image Segmentation TechniquesBrain Tumor Detection and Classification

Cited by 3,498

Traumatic brain injury: integrated approaches to improve prevention, clinical care, and research

Maas, A. I. R., Menon, D. K., Adelson, P. D., Andelic, N., Bell, M. J., Belli, A., Bragge, P., Brazinova, A., Büki, A., Chesnut, R. M., et al. (2017) · The Lancet Neurology

Traumatic Brain Injury ResearchTraumatic Brain Injury and Neurovascular Disturbances

Cited by 2,609

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

Bakas, S., Reyes, M., Jakab, A., Bauer, S., Rempfler, M., Crimi, A., Shinohara, R. T., Berger, C., Ha, S. M., Rozycki, M., et al. (2018) · Apollo - University of Cambridge Repository

Brain Tumor Detection and ClassificationMedical Image Segmentation Techniques

Cited by 1,441

Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the brats challenge

Bakas, S., Reyes, M., Jakab, A., Bauer, S., Rempfler, M., Crimi, A., Shinohara, R. T., Berger, C., Ha, S. M., Rozycki, M., et al. (2018) · arXiv

Brain Tumor Detection and ClassificationMedical Image Segmentation Techniques

Cited by 1,441

Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI

Vasey, B., Nagendran, M., Campbell, B., Clifton, D. A., Collins, G. S., Denaxas, S., Denniston, A. K., Faes, L., Geerts, B., Ibrahim, M., et al. (2022) · Nature Medicine

Artificial Intelligence in Healthcare and EducationMeta-analysis and systematic reviews

Cited by 1,039

Anatomically constrained neural networks (ACNNS): application to cardiac image enhancement and segmentation

Oktay, O., Ferrante, E., Kamnitsas, K., Heinrich, M., Bai, W., Caballero, J., Cook, S. A., de Marvao, A., Dawes, T., O'Regan, D. P., et al. (2018) · IEEE Transactions on Medical Imaging

Medical Image Segmentation TechniquesAdvanced Neural Network Applications

Cited by 751

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

Baumgartner, C. F., Kamnitsas, K., Matthew, J., Fletcher, T. P., Smith, S., Koch, L. M., Kainz, B., & Rueckert, D. (2017) · IEEE Transactions on Medical Imaging

Medical Image Segmentation TechniquesArtificial Intelligence in Healthcare and Education

Cited by 416

DeepCut: Object Segmentation From Bounding Box Annotations Using Convolutional Neural Networks

Rajchl, M., Lee, M. C. H., Oktay, O., Kamnitsas, K., Passerat-Palmbach, J., Bai, W., Damodaram, M., Rutherford, M. A., Hajnal, J. V., Kainz, B., et al. (2017) · IEEE Transactions on Medical Imaging

Medical Image Segmentation TechniquesAdvanced Neural Network Applications

Cited by 410

Federated learning enables big data for rare cancer boundary detection

Pati, S., Baid, U., Edwards, B., Sheller, M., Wang, S.-H., Reina, G. A., Foley, P., Gruzdev, A., Karkada, D., Davatzikos, C., et al. (2022) · Nature Communications

Privacy-Preserving Technologies in DataBrain Tumor Detection and Classification

Cited by 394

DeepMedic for Brain Tumor Segmentation

Kamnitsas, K., Ferrante, E., Parisot, S., Ledig, C., Nori, A. V., Criminisi, A., Rueckert, D., & Glocker, B. (2016) · Lecture Notes in Computer Science

Brain Tumor Detection and ClassificationMedical Image Segmentation Techniques

Cited by 346

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

Oktay, O., Bai, W., Lee, M., Guerrero, R., Kamnitsas, K., Caballero, J., de Marvao, A., Cook, S., O’Regan, D., & Rueckert, D. (2016) · Lecture Notes in Computer Science

Advanced Image Processing TechniquesMedical Image Segmentation Techniques

Cited by 246

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

Monteiro, M., Newcombe, V. F. J., Mathieu, F., Adatia, K., Kamnitsas, K., Ferrante, E., Das, T., Whitehouse, D., Rueckert, D., Menon, D. K., et al. (2020) · The Lancet Digital Health

Medical Image Segmentation TechniquesBrain Tumor Detection and Classification

Cited by 162

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

Valindria, V. V., Lavdas, I., Bai, W., Kamnitsas, K., Aboagye, E. O., Rockall, A. G., Rueckert, D., & Glocker, B. (2017) · IEEE Transactions on Medical Imaging

Medical Image Segmentation TechniquesArtificial Intelligence in Healthcare and Education

Cited by 152

Autofocus Layer for Semantic Segmentation

Qin, Y., Kamnitsas, K., Ancha, S., Nanavati, J., Cottrell, G., Criminisi, A., & Nori, A. (2018) · arXiv

Advanced Neural Network ApplicationsMedical Image Segmentation Techniques

Cited by 135

Autofocus Layer for Semantic Segmentation

Qin, Y., Kamnitsas, K., Ancha, S., Nanavati, J., Cottrell, G., Criminisi, A., & Nori, A. (2018) · Lecture Notes in Computer Science

Advanced Neural Network ApplicationsMedical Image Segmentation Techniques

Cited by 135

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

Alansary, A., Kamnitsas, K., Davidson, A., Khlebnikov, R., Rajchl, M., Malamateniou, C., Rutherford, M., Hajnal, J. V., Glocker, B., Rueckert, D., et al. (2016) · Lecture Notes in Computer Science

Medical Image Segmentation TechniquesBrain Tumor Detection and Classification

Cited by 77

Estimating Model Performance under Domain Shifts with Class-Specific Confidence Scores

Li, Z., Kamnitsas, K., Islam, M., Chen, C., & Glocker, B. (2022) · arXiv

Domain Adaptation and Few-Shot LearningImbalanced Data Classification Techniques

Cited by 16

Deep neural network to locate and segment brain tumors outperformed the expert technicians who created the training data

Mitchell, J. R., Kamnitsas, K., Singleton, K. W., Whitmire, S. A., Clark-Swanson, K. R., Ranjbar, S., Rickertsen, C. R., Johnston, S. K., Egan, K. M., Rollison, D. E., et al. (2020) · Journal of Medical Imaging

Brain Tumor Detection and ClassificationMedical Image Segmentation Techniques

Cited by 15

$$\mathrm {IterMask^2}$$: Iterative Unsupervised Anomaly Segmentation via Spatial and Frequency Masking for Brain Lesions in MRI

Liang, Z., Guo, X., Noble, J. A., & Kamnitsas, K. (2024) · Lecture Notes in Computer Science

Medical Image Segmentation TechniquesBrain Tumor Detection and Classification

Cited by 10

IterMask3D: Unsupervised anomaly detection and segmentation with test-time iterative mask refinement in 3D brain MRI

Liang, Z., Guo, X., Xu, W., Ibrahim, Y., Voets, N., Pretorius, P. M., Noble, J. A., & Kamnitsas, K. (2026) · Medical Image Analysis

Medical Image Segmentation TechniquesAnomaly Detection Techniques and Applications

Cited by 3

IterMask3D: Unsupervised Anomaly Detection and Segmentation with Test-Time Iterative Mask Refinement in 3D Brain MR

Liang, Z., Guo, X., Xu, W., Ibrahim, Y., Voets, N., Pretorius, P. M., Noble, J. A., & Kamnitsas, K. (2025) · arXiv

Medical Image Segmentation TechniquesAnomaly Detection Techniques and Applications

Cited by 0