Showing 50 publications by Konstantinos Kamnitsas
Radiomics for Treatment Planning in Liver Cancers.
Mian A, Kamnitsas K & Gordon-Weeks A (2025), JAMA surgery
BibTeX
@article{radiomicsfortre-2025/2,
title={Radiomics for Treatment Planning in Liver Cancers.},
author={Mian A, Kamnitsas K & Gordon-Weeks A},
journal={JAMA surgery},
year = "2025"
}
Evaluating Reliability in Medical DNNs: A Critical Analysis of Feature and Confidence-Based OOD Detection
Anthony H & Kamnitsas K (2025), 15167, 160-170
BibTeX
@misc{evaluatingrelia-2025/,
title={Evaluating Reliability in Medical DNNs: A Critical Analysis of Feature and Confidence-Based OOD Detection},
author={Anthony H & Kamnitsas K},
year = "2025"
}
Is Your Style Transfer Doing Anything Useful? An Investigation into Hippocampus Segmentation and the Role of Preprocessing
Kalabizadeh H, Griffanti L, Yeung P-H, Voets N, Gillis G et al. (2025), 15266, 155-165
BibTeX
@misc{isyourstyletran-2025/,
title={Is Your Style Transfer Doing Anything Useful? An Investigation into Hippocampus Segmentation and the Role of Preprocessing},
author={Kalabizadeh H, Griffanti L, Yeung P-H, Voets N, Gillis G et al.},
year = "2025"
}
Quality Control for Radiology Report Generation Models via Auxiliary Auditing Components
Warr H, Ibrahim Y, McGowan DR & Kamnitsas K (2025), 15167, 70-80
BibTeX
@misc{qualitycontrolf-2025/,
title={Quality Control for Radiology Report Generation Models via Auxiliary Auditing Components},
author={Warr H, Ibrahim Y, McGowan DR & Kamnitsas K},
year = "2025"
}
As firm as their foundations: creating transferable adversarial examples across downstream tasks with CLIP
Hu A, Gu J, Pinto F, Kamnitsas K & Torr PHS (2024), Proceedings of the 35th British Machine Vision Conference (BMVC 2024)
BibTeX
@inproceedings{asfirmastheirfo-2024/12,
title={As firm as their foundations: creating transferable adversarial examples across downstream tasks with CLIP},
author={Hu A, Gu J, Pinto F, Kamnitsas K & Torr PHS},
booktitle={35th British Machine Vision Conference (BMVC 2024)},
year = "2024"
}
FedPIA -- Permuting and Integrating Adapters leveraging Wasserstein Barycenters for Finetuning Foundation Models in Multi-Modal Federated Learning
Saha P, Mishra D, Wagner F, Kamnitsas K & Noble JA (2024)
BibTeX
@misc{fedpiapermuting-2024/12,
title={FedPIA -- Permuting and Integrating Adapters leveraging Wasserstein Barycenters for Finetuning Foundation Models in Multi-Modal Federated Learning},
author={Saha P, Mishra D, Wagner F, Kamnitsas K & Noble JA},
year = "2024"
}
SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation
Zhu J, Wu J, Ouyang C, Kamnitsas K & Noble A (2024)
BibTeX
@misc{spaefficientuse-2024/11,
title={SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation},
author={Zhu J, Wu J, Ouyang C, Kamnitsas K & Noble A},
year = "2024"
}
F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics
Saha P, Wagner F, Mishra D, Peng C, Thakur A et al. (2024)
BibTeX
@misc{focusfederatedf-2024/11,
title={F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics},
author={Saha P, Wagner F, Mishra D, Peng C, Thakur A et al.},
year = "2024"
}
Is your style transfer doing anything useful? an investigation into hippocampus segmentation and the role of preprocessing
Kalabizadeh H, Griffanti L, Yeung P-H, Voets N, Gillis G et al. (2024)
BibTeX
@misc{isyourstyletran-2024/8,
title={Is your style transfer doing anything useful? an investigation into hippocampus segmentation and the role of preprocessing},
author={Kalabizadeh H, Griffanti L, Yeung P-H, Voets N, Gillis G et al.},
year = "2024"
}
Evaluating reliability in medical DNNs: a critical analysis of feature and confidence-based OOD detection
Anthony H & Kamnitsas K (2024)
BibTeX
@misc{evaluatingrelia-2024/8,
title={Evaluating reliability in medical DNNs: a critical analysis of feature and confidence-based OOD detection},
author={Anthony H & Kamnitsas K},
year = "2024"
}
An organism starts with a single pix-cell: a neural cellular diffusion for high-resolution image synthesis
Elbatel M, Kamnitsas K & Li X (2024)
BibTeX
@misc{anorganismstart-2024/7,
title={An organism starts with a single pix-cell: a neural cellular diffusion for high-resolution image synthesis},
author={Elbatel M, Kamnitsas K & Li X},
year = "2024"
}
Quality control for radiology report generation models via auxiliary auditing components
Warr H, Ibrahim Y, McGowan DR & Kamnitsas K (2024)
BibTeX
@misc{qualitycontrolf-2024/7,
title={Quality control for radiology report generation models via auxiliary auditing components},
author={Warr H, Ibrahim Y, McGowan DR & Kamnitsas K},
year = "2024"
}
IterMask2: iterative unsupervised anomaly segmentation via spatial and frequency masking for brain lesions in MRI
Liang Z, Guo X, Noble JA & Kamnitsas K (2024)
BibTeX
@misc{itermaskiterati-2024/6,
title={IterMask2: iterative unsupervised anomaly segmentation via spatial and frequency masking for brain lesions in MRI},
author={Liang Z, Guo X, Noble JA & Kamnitsas K},
year = "2024"
}
Feasibility of Federated Learning from Client Databases with Different Brain Diseases and MRI Modalities
Wagner F, Xu W, Saha P, Liang Z, Whitehouse D et al. (2024)
BibTeX
@misc{feasibilityoffe-2024/6,
title={Feasibility of Federated Learning from Client Databases with Different Brain Diseases and MRI Modalities},
author={Wagner F, Xu W, Saha P, Liang Z, Whitehouse D et al.},
year = "2024"
}
Feasibility and benefits of joint learning from MRI databases with different brain diseases and modalities for segmentation
Xu W, Moffat M, Seale T, Liang Z, Wagner F et al. (2024)
BibTeX
@misc{feasibilityandb-2024/5,
title={Feasibility and benefits of joint learning from MRI databases with different brain diseases and modalities for segmentation},
author={Xu W, Moffat M, Seale T, Liang Z, Wagner F et al.},
year = "2024"
}
Semi-supervised learning for deep causal generative models
Ibrahim Y, Warr H & Kamnitsas K (2024)
BibTeX
@misc{semisupervisedl-2024/3,
title={Semi-supervised learning for deep causal generative models},
author={Ibrahim Y, Warr H & Kamnitsas K},
year = "2024"
}
As firm as their foundations: can open-sourced foundation models be used to create adversarial examples for downstream tasks?
Hu A, Gu J, Pinto F, Kamnitsas K & Torr P (2024)
BibTeX
@misc{asfirmastheirfo-2024/3,
title={As firm as their foundations: can open-sourced foundation models be used to create adversarial examples for downstream tasks?},
author={Hu A, Gu J, Pinto F, Kamnitsas K & Torr P},
year = "2024"
}
Modality cycles with masked conditional diffusion for unsupervised anomaly segmentation in MRI
Liang Z, Anthony H, Wagner F & Kamnitsas K (2024), Proceedings of the 2nd International Workshop on Applications of Medical AI (MICCAI - AMAI 2023), 14394, 168-181
BibTeX
@inproceedings{modalitycyclesw-2024/2,
title={Modality cycles with masked conditional diffusion for unsupervised anomaly segmentation in MRI},
author={Liang Z, Anthony H, Wagner F & Kamnitsas K},
booktitle={2nd International Workshop on Applications of Medical AI (MICCAI - AMAI 2023)},
pages={168-181},
year = "2024"
}
Examining Modality Incongruity in Multimodal Federated Learning for Medical Vision and Language-based Disease Detection
Saha P, Mishra D, Wagner F, Kamnitsas K & Noble JA (2024)
BibTeX
@misc{examiningmodali-2024/2,
title={Examining Modality Incongruity in Multimodal Federated Learning for Medical Vision and Language-based Disease Detection},
author={Saha P, Mishra D, Wagner F, Kamnitsas K & Noble JA},
year = "2024"
}
Preface DART 2023
Koch L, Cardoso MJ, Ferrante E, Islam M, Jiang M et al. (2024), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 14293 LNCS, v-vi
BibTeX
@article{prefacedart-2024/1,
title={Preface DART 2023},
author={Koch L, Cardoso MJ, Ferrante E, Islam M, Jiang M et al.},
journal={Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume={14293 LNCS},
pages={v-vi},
year = "2024"
}
Feasibility and benefits of joint learning from MRI databases with different brain diseases and modalities for segmentation
Xu W, Moffat M, Seale T, Liang Z, Wagner F et al. (2024), Proceedings of Machine Learning Research, 250, 1771-1784
BibTeX
@inproceedings{feasibilityandb-2024/1,
title={Feasibility and benefits of joint learning from MRI databases with different brain diseases and modalities for segmentation},
author={Xu W, Moffat M, Seale T, Liang Z, Wagner F et al.},
pages={1771-1784},
year = "2024"
}
IterMask2: Iterative Unsupervised Anomaly Segmentation via Spatial and Frequency Masking for Brain Lesions in MRI
Liang Z, Guo X, Noble JA & Kamnitsas K (2024), 15008, 339-348
BibTeX
@misc{itermaskiterati-2024/,
title={IterMask2: Iterative Unsupervised Anomaly Segmentation via Spatial and Frequency Masking for Brain Lesions in MRI},
author={Liang Z, Guo X, Noble JA & Kamnitsas K},
year = "2024"
}
Semi-Supervised Learning for Deep Causal Generative Models
Ibrahim Y, Warr H & Kamnitsas K (2024), 15012, 294-303
BibTeX
@misc{semisupervisedl-2024/,
title={Semi-Supervised Learning for Deep Causal Generative Models},
author={Ibrahim Y, Warr H & Kamnitsas K},
year = "2024"
}
An Organism Starts with a Single Pix-Cell: A Neural Cellular Diffusion for High-Resolution Image Synthesis
Elbatel M, Kamnitsas K & Li X (2024), 15001, 656-666
BibTeX
@misc{anorganismstart-2024/,
title={An Organism Starts with a Single Pix-Cell: A Neural Cellular Diffusion for High-Resolution Image Synthesis},
author={Elbatel M, Kamnitsas K & Li X},
year = "2024"
}
Joint Optimization of Class-Specific Training- and Test-Time Data Augmentation in Segmentation.
Li Z, Kamnitsas K, Dou Q, Qin C & Glocker B (2023), IEEE transactions on medical imaging, 42(11), 3323-3335
BibTeX
@article{jointoptimizati-2023/11,
title={Joint Optimization of Class-Specific Training- and Test-Time Data Augmentation in Segmentation.},
author={Li Z, Kamnitsas K, Dou Q, Qin C & Glocker B},
journal={IEEE transactions on medical imaging},
volume={42},
pages={3323-3335},
year = "2023"
}
Post-deployment adaptation with access to source data via federated learning and source-target remote gradient alignment
Wagner F, Li Z, Saha P & Kamnitsas K (2023), Machine Learning in Medical Imaging, 253-263
BibTeX
@inproceedings{postdeploymenta-2023/10,
title={Post-deployment adaptation with access to source data via federated learning and source-target remote gradient alignment},
author={Wagner F, Li Z, Saha P & Kamnitsas K},
booktitle={Machine Learning in Medical Imaging 14th International Workshop, MLMI 2023},
pages={253-263},
year = "2023"
}
On the use of Mahalanobis distance for out-of-distribution detection with neural networks for medical imaging
Anthony H & Kamnitsas K (2023), 136-146
BibTeX
@misc{ontheuseofmahal-2023/10,
title={On the use of Mahalanobis distance for out-of-distribution detection with neural networks for medical imaging},
author={Anthony H & Kamnitsas K},
year = "2023"
}
On the use of Mahalanobis distance for out-of-distribution detection with neural networks for medical imaging
Anthony H & Kamnitsas K (2023)
BibTeX
@misc{ontheuseofmahal-2023/9,
title={On the use of Mahalanobis distance for out-of-distribution detection with neural networks for medical imaging},
author={Anthony H & Kamnitsas K},
year = "2023"
}
Post-deployment adaptation with access to source data via federated learning and source-target remote gradient alignment
Wagner F, Li Z, Saha P & Kamnitsas K (2023)
BibTeX
@misc{postdeploymenta-2023/8,
title={Post-deployment adaptation with access to source data via federated learning and source-target remote gradient alignment},
author={Wagner F, Li Z, Saha P & Kamnitsas K},
year = "2023"
}
Modality cycles with masked conditional diffusion for unsupervised anomaly segmentation in MRI
Liang Z, Anthony H, Wagner F & Kamnitsas K (2023)
BibTeX
@misc{modalitycyclesw-2023/8,
title={Modality cycles with masked conditional diffusion for unsupervised anomaly segmentation in MRI},
author={Liang Z, Anthony H, Wagner F & Kamnitsas K},
year = "2023"
}
A Review of the Metrics Used to Assess Auto-Contouring Systems in Radiotherapy.
Mackay K, Bernstein D, Glocker B, Kamnitsas K & Taylor A (2023), Clinical oncology (Royal College of Radiologists (Great Britain)), 35(6), 354-369
BibTeX
@article{areviewofthemet-2023/6,
title={A Review of the Metrics Used to Assess Auto-Contouring Systems in Radiotherapy.},
author={Mackay K, Bernstein D, Glocker B, Kamnitsas K & Taylor A},
journal={Clinical oncology (Royal College of Radiologists (Great Britain))},
volume={35},
pages={354-369},
year = "2023"
}
Context Label Learning: Improving Background Class Representations in Semantic Segmentation.
Li Z, Kamnitsas K, Ouyang C, Chen C & Glocker B (2023), IEEE transactions on medical imaging, 42(6), 1885-1896
BibTeX
@article{contextlabellea-2023/6,
title={Context Label Learning: Improving Background Class Representations in Semantic Segmentation.},
author={Li Z, Kamnitsas K, Ouyang C, Chen C & Glocker B},
journal={IEEE transactions on medical imaging},
volume={42},
pages={1885-1896},
year = "2023"
}
Joint optimization of class-specific training- and test-time data augmentation in segmentation
Li Z, Kamnitsas K, Dou Q, Qin C & Glocker B (2023)
BibTeX
@misc{jointoptimizati-2023/5,
title={Joint optimization of class-specific training- and test-time data augmentation in segmentation},
author={Li Z, Kamnitsas K, Dou Q, Qin C & Glocker B},
year = "2023"
}
Context label learning: improving background class representations in semantic segmentation
Li Z, Kamnitsas K, Ouyang C, Chen C & Glocker B (2022)
BibTeX
@misc{contextlabellea-2022/12,
title={Context label learning: improving background class representations in semantic segmentation},
author={Li Z, Kamnitsas K, Ouyang C, Chen C & Glocker B},
year = "2022"
}
Federated learning enables big data for rare cancer boundary detection.
Pati S, Baid U, Edwards B, Sheller M, Wang S-H et al. (2022), Nature communications, 13(1), 7346-7346
BibTeX
@article{federatedlearni-2022/12,
title={Federated learning enables big data for rare cancer boundary detection.},
author={Pati S, Baid U, Edwards B, Sheller M, Wang S-H et al.},
journal={Nature communications},
volume={13},
pages={7346-7346},
year = "2022"
}
Estimating model performance under domain shifts with class-specific confidence scores
Li Z, Kamnitsas K, Islam M, Chen C & Glocker B (2022), Proceedings of the 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022), 693-703
BibTeX
@inproceedings{estimatingmodel-2022/9,
title={Estimating model performance under domain shifts with class-specific confidence scores},
author={Li Z, Kamnitsas K, Islam M, Chen C & Glocker B},
booktitle={25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022)},
pages={693-703},
year = "2022"
}
Estimating model performance under domain shifts with class-specific confidence scores
Li Z, Kamnitsas K, Islam M, Chen C & Glocker B (2022)
BibTeX
@misc{estimatingmodel-2022/7,
title={Estimating model performance under domain shifts with class-specific confidence scores},
author={Li Z, Kamnitsas K, Islam M, Chen C & Glocker B},
year = "2022"
}
Distributional Gaussian Processes layers for out-of-distribution detection
Popescu SG, Sharp DJ, Cole JH, Kamnitsas K & Glocker B (2022), MELBA Journal, 1(IPMI 2021), 1-64
BibTeX
@article{distributionalg-2022/6,
title={Distributional Gaussian Processes layers for out-of-distribution detection},
author={Popescu SG, Sharp DJ, Cole JH, Kamnitsas K & Glocker B},
journal={MELBA Journal},
volume={1},
pages={1-64},
publisher={Machine Learning for Biomedical Imaging},
year = "2022"
}
Distributional Gaussian Processes layers for out-of-distribution detection
Popescu SG, Sharp DJ, Cole JH, Kamnitsas K & Glocker B (2022)
BibTeX
@misc{distributionalg-2022/6,
title={Distributional Gaussian Processes layers for out-of-distribution detection},
author={Popescu SG, Sharp DJ, Cole JH, Kamnitsas K & Glocker B},
year = "2022"
}
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 DA, Collins GS et al. (2022), BMJ, 377
BibTeX
@article{reportingguidel-2022/5,
title={Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI},
author={Vasey B, Nagendran M, Campbell B, Clifton DA, Collins GS et al.},
journal={BMJ},
volume={377},
number={e070904},
publisher={BMJ Publishing Group},
year = "2022"
}
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 DA, Collins GS et al. (2022), Nature medicine, 28(5), 924-933
BibTeX
@article{reportingguidel-2022/5,
title={Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI.},
author={Vasey B, Nagendran M, Campbell B, Clifton DA, Collins GS et al.},
journal={Nature medicine},
volume={28},
pages={924-933},
year = "2022"
}
Federated learning enables big data for rare cancer boundary detection
Pati S, Baid U, Edwards B, Sheller M, Wang S-H et al. (2022)
BibTeX
@misc{federatedlearni-2022/4,
title={Federated learning enables big data for rare cancer boundary detection},
author={Pati S, Baid U, Edwards B, Sheller M, Wang S-H et al.},
year = "2022"
}
Preface
Cardoso MJ, Dou Q, Islam M, Kamnitsas K, Koch L et al. (2022), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13542 LNCS, v-vi
BibTeX
@article{preface-2022/1,
title={Preface},
author={Cardoso MJ, Dou Q, Islam M, Kamnitsas K, Koch L et al.},
journal={Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume={13542 LNCS},
pages={v-vi},
year = "2022"
}
Relationship of admission blood proteomic biomarkers levels to lesion type and lesion burden in traumatic brain injury: a CENTER-TBI study
Whitehouse DP, Monteiro M, Czeiter E, Vyvere TV, Valerio F et al. (2021), EBioMedicine, 75
BibTeX
@article{relationshipofa-2021/12,
title={Relationship of admission blood proteomic biomarkers levels to lesion type and lesion burden in traumatic brain injury: a CENTER-TBI study},
author={Whitehouse DP, Monteiro M, Czeiter E, Vyvere TV, Valerio F et al.},
journal={EBioMedicine},
volume={75},
number={103777},
publisher={Elsevier},
year = "2021"
}
Biomarkers for Traumatic Brain Injury: Data Standards and Statistical Considerations.
Huie JR, Mondello S, Lindsell CJ, Antiga L, Yuh EL et al. (2021), Journal of neurotrauma, 38(18), 2514-2529
BibTeX
@article{biomarkersfortr-2021/9,
title={Biomarkers for Traumatic Brain Injury: Data Standards and Statistical Considerations.},
author={Huie JR, Mondello S, Lindsell CJ, Antiga L, Yuh EL et al.},
journal={Journal of neurotrauma},
volume={38},
pages={2514-2529},
year = "2021"
}
Transductive image segmentation: Self-training and effect of uncertainty estimation
Kamnitsas K, Winzeck S, Kornaropoulos EN, Whitehouse D, Englman C et al. (2021)
BibTeX
@misc{transductiveima-2021/7,
title={Transductive image segmentation: Self-training and effect of uncertainty estimation},
author={Kamnitsas K, Winzeck S, Kornaropoulos EN, Whitehouse D, Englman C et al.},
year = "2021"
}
Learning from Partially Overlapping Labels: Image Segmentation under Annotation Shift
Filbrandt G, Kamnitsas K, Bernstein D, Taylor A & Glocker B (2021)
BibTeX
@misc{learningfrompar-2021/7,
title={Learning from Partially Overlapping Labels: Image Segmentation under Annotation Shift},
author={Filbrandt G, Kamnitsas K, Bernstein D, Taylor A & Glocker B},
year = "2021"
}
Confidence-based Out-of-Distribution Detection: A Comparative Study and Analysis
Berger C, Paschali M, Glocker B & Kamnitsas K (2021)
BibTeX
@misc{confidencebased-2021/7,
title={Confidence-based Out-of-Distribution Detection: A Comparative Study and Analysis},
author={Berger C, Paschali M, Glocker B & Kamnitsas K},
year = "2021"
}
Distributional Gaussian Process Layers for Outlier Detection in Image Segmentation
Popescu SG, Sharp DJ, Cole JH, Kamnitsas K & Glocker B (2021)
BibTeX
@misc{distributionalg-2021/4,
title={Distributional Gaussian Process Layers for Outlier Detection in Image Segmentation},
author={Popescu SG, Sharp DJ, Cole JH, Kamnitsas K & Glocker B},
year = "2021"
}
Analyzing Overfitting Under Class Imbalance in Neural Networks for Image Segmentation.
Li Z, Kamnitsas K & Glocker B (2021), IEEE transactions on medical imaging, 40(3), 1065-1077
BibTeX
@article{analyzingoverfi-2021/3,
title={Analyzing Overfitting Under Class Imbalance in Neural Networks for Image Segmentation.},
author={Li Z, Kamnitsas K & Glocker B},
journal={IEEE transactions on medical imaging},
volume={40},
pages={1065-1077},
year = "2021"
}