K. Kamnitsas

Publications64
h-index24
Citations5,779
Highly Influential Citations309
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Efficient multi‐scale 3D CNN with fully connected CRF for accurate brain lesion segmentation
TLDR
An efficient and effective dense training scheme which joins the processing of adjacent image patches into one pass through the network while automatically adapting to the inherent class imbalance present in the data, and improves on the state-of-the‐art for all three applications.
Domain Generalization via Model-Agnostic Learning of Semantic Features
TLDR
This work investigates the challenging problem of domain generalization, i.e., training a model on multi-domain source data such that it can directly generalize to target domains with unknown statistics, and adopts a model-agnostic learning paradigm with gradient-based meta-train and meta-test procedures to expose the optimization to domain shift.
Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation
TLDR
This work proposes a generic training strategy that incorporates anatomical prior knowledge into CNNs through a new regularisation model, which is trained end-to-end and demonstrates how the learnt deep models of 3-D shapes can be interpreted and used as biomarkers for classification of cardiac pathologies.
Unsupervised domain adaptation in brain lesion segmentation with adversarial networks
TLDR
This work investigates unsupervised domain adaptation using adversarial neural networks to train a segmentation method which is more robust to differences in the input data, and which does not require any annotations on the test domain.
Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation
TLDR
This paper explores Ensembles of Multiple Models and Architectures (EMMA) for robust performance through aggregation of predictions from a wide range of methods to reduce the influence of the meta-parameters of individual models and the risk of overfitting the configuration to a particular database.
SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound
TLDR
A novel method based on convolutional neural networks is proposed, which can automatically detect 13 fetal standard views in freehand 2-D ultrasound data as well as provide a localization of the fetal structures via a bounding box while providing optimal output for the localization task.
Traumatic brain injury: integrated approaches to improve prevention, clinical care, and research
TLDR
The InTBIR Participants and Investigators have provided informed consent for the study to take place in Poland.
DeepMedic for Brain Tumor Segmentation
TLDR
DeepMedic, a 3D CNN architecture previously presented for lesion segmentation, is employed, which is further improved by adding residual connections, aiming to shed some light on requirements for employing such a system.
DeepCut: Object Segmentation From Bounding Box Annotations Using Convolutional Neural Networks
TLDR
This paper proposes a method to obtain pixelwise object segmentations given an image dataset labelled weak annotations, in this case bounding boxes, and test its applicability to solve brain and lung segmentation problems on a challenging fetal magnetic resonance dataset.
Multi-input Cardiac Image Super-Resolution Using Convolutional Neural Networks
TLDR
Experimental results show that the CNN model outperforms state-of-the-art SR methods in terms of image quality while being computationally efficient and that image segmentation and motion tracking benefits more from SR-CNN when it is used as an initial upscaling method than conventional interpolation methods for the subsequent analysis.
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