3.6k citations · 5.7k across the 34 of their papers we have counts for
27 papers · 1 filter
Uncertainty as a Proxy for Semantic Correctness in Diffusion-Based Medical Image Synthesis
Yuxuan Ou, Konstantinos Kamnitsas, OxAAA Study +3
Diffusion models can synthesise contrast-enhanced CT (CECT) from non-contrast CT (NCCT), avoiding contrast administration and its environmental and patient-access costs. However, v…
BrainIAC: Interactive 3D Brain Lesion Segmentation across Heterogeneous MRI Modalities with Online Adaptation
Wentian Xu, Anthony P Addison, Ziyun Liang +3
Brain lesion segmentation is a fundamental task in medical image analysis, playing a critical role in diagnosis, treatment planning, and longitudinal disease monitoring. Yet existi…
Post-Deployment Adaptation with Access to Source Data via Federated Learning and Source-Target Remote Gradient Alignment
Felix Wagner, Zeju Li, Pramit Saha +1
Deployment of Deep Neural Networks in medical imaging is hindered by distribution shift between training data and data processed after deployment, causing performance degradation.…
Joint Optimization of Class-Specific Training- and Test-Time Data Augmentation in Segmentation
Zeju Li, Konstantinos Kamnitsas, Qi Dou +2
This paper presents an effective and general data augmentation framework for medical image segmentation. We adopt a computationally efficient and data-efficient gradient-based meta…
Context Label Learning: Improving Background Class Representations in Semantic Segmentation
Zeju Li, Konstantinos Kamnitsas, Cheng Ouyang +2
Background samples provide key contextual information for segmenting regions of interest (ROIs). However, they always cover a diverse set of structures, causing difficulties for th…
Estimating Model Performance under Domain Shifts with Class-Specific Confidence Scores
Zeju Li, Konstantinos Kamnitsas, Mobarakol Islam +2
Machine learning models are typically deployed in a test setting that differs from the training setting, potentially leading to decreased model performance because of domain shift.…
Distributional Gaussian Processes Layers for Out-of-Distribution Detection
Sebastian G. Popescu, David J. Sharp, James H. Cole +2
Machine learning models deployed on medical imaging tasks must be equipped with out-of-distribution detection capabilities in order to avoid erroneous predictions. It is unsure whe…
Transductive image segmentation: Self-training and effect of uncertainty estimation
Konstantinos Kamnitsas, Stefan Winzeck, Evgenios N. Kornaropoulos +9
Semi-supervised learning (SSL) uses unlabeled data during training to learn better models. Previous studies on SSL for medical image segmentation focused mostly on improving model…
Learning from Partially Overlapping Labels: Image Segmentation under Annotation Shift
Gregory Filbrandt, Konstantinos Kamnitsas, David Bernstein +2
Scarcity of high quality annotated images remains a limiting factor for training accurate image segmentation models. While more and more annotated datasets become publicly availabl…
Confidence-based Out-of-Distribution Detection: A Comparative Study and Analysis
Christoph Berger, Magdalini Paschali, Ben Glocker +1
Image classification models deployed in the real world may receive inputs outside the intended data distribution. For critical applications such as clinical decision making, it is…
Analyzing Overfitting under Class Imbalance in Neural Networks for Image Segmentation
Zeju Li, Konstantinos Kamnitsas, Ben Glocker
Class imbalance poses a challenge for developing unbiased, accurate predictive models. In particular, in image segmentation neural networks may overfit to the foreground samples fr…
Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty
Miguel Monteiro, Loïc Le Folgoc, Daniel Coelho de Castro +5
In image segmentation, there is often more than one plausible solution for a given input. In medical imaging, for example, experts will often disagree about the exact location of o…
Domain Generalization via Model-Agnostic Learning of Semantic Features
Qi Dou, Daniel C. Castro, Konstantinos Kamnitsas +1
Generalization capability to unseen domains is crucial for machine learning models when deploying to real-world conditions. We investigate the challenging problem of domain general…
Multiple Landmark Detection using Multi-Agent Reinforcement Learning
Athanasios Vlontzos, Amir Alansary, Konstantinos Kamnitsas +2
The detection of anatomical landmarks is a vital step for medical image analysis and applications for diagnosis, interpretation and guidance. Manual annotation of landmarks is a te…
Towards continual learning in medical imaging
Chaitanya Baweja, Ben Glocker, Konstantinos Kamnitsas
This work investigates continual learning of two segmentation tasks in brain MRI with neural networks. To explore in this context the capabilities of current methods for countering…
Generative adversarial networks and adversarial methods in biomedical image analysis
Jelmer M. Wolterink, Konstantinos Kamnitsas, Christian Ledig +1
Generative adversarial networks (GANs) and other adversarial methods are based on a game-theoretical perspective on joint optimization of two neural networks as players in a game.…
Autofocus Layer for Semantic Segmentation
Yao Qin, Konstantinos Kamnitsas, Siddharth Ancha +4
We propose the autofocus convolutional layer for semantic segmentation with the objective of enhancing the capabilities of neural networks for multi-scale processing. Autofocus lay…
Automatic View Planning with Multi-scale Deep Reinforcement Learning Agents
Amir Alansary, Loic Le Folgoc, Ghislain Vaillant +11
We propose a fully automatic method to find standardized view planes in 3D image acquisitions. Standard view images are important in clinical practice as they provide a means to pe…
Domain Adaptation for MRI Organ Segmentation using Reverse Classification Accuracy
Vanya V. Valindria, Ioannis Lavdas, Wenjia Bai +5
The variations in multi-center data in medical imaging studies have brought the necessity of domain adaptation. Despite the advancement of machine learning in automatic segmentatio…
Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation
Konstantinos Kamnitsas, Wenjia Bai, Enzo Ferrante +8
Deep learning approaches such as convolutional neural nets have consistently outperformed previous methods on challenging tasks such as dense, semantic segmentation. However, the v…
Anatomically Constrained Neural Networks (ACNN): Application to Cardiac Image Enhancement and Segmentation
Ozan Oktay, Enzo Ferrante, Konstantinos Kamnitsas +10
Incorporation of prior knowledge about organ shape and location is key to improve performance of image analysis approaches. In particular, priors can be useful in cases where image…
Context-Sensitive Super-Resolution for Fast Fetal Magnetic Resonance Imaging
Steven McDonagh, Benjamin Hou, Konstantinos Kamnitsas +5
3D Magnetic Resonance Imaging (MRI) is often a trade-off between fast but low-resolution image acquisition and highly detailed but slow image acquisition. Fast imaging is required…
Reverse Classification Accuracy: Predicting Segmentation Performance in the Absence of Ground Truth
Vanya V. Valindria, Ioannis Lavdas, Wenjia Bai +5
When integrating computational tools such as automatic segmentation into clinical practice, it is of utmost importance to be able to assess the level of accuracy on new data, and i…
Unsupervised domain adaptation in brain lesion segmentation with adversarial networks
Konstantinos Kamnitsas, Christian Baumgartner, Christian Ledig +8
Significant advances have been made towards building accurate automatic segmentation systems for a variety of biomedical applications using machine learning. However, the performan…