74 citations · 392 across the 18 of their papers we have counts for
18 papers
Weakly Supervised Estimation of Shadow Confidence Maps in Fetal Ultrasound Imaging
Qingjie Meng, Matthew Sinclair, Veronika Zimmer +11
Detecting acoustic shadows in ultrasound images is important in many clinical and engineering applications. Real-time feedback of acoustic shadows can guide sonographers to a stand…
Learning Interpretable Anatomical Features Through Deep Generative Models: Application to Cardiac Remodeling
Carlo Biffi, Ozan Oktay, Giacomo Tarroni +9
Alterations in the geometry and function of the heart define well-established causes of cardiovascular disease. However, current approaches to the diagnosis of cardiovascular disea…
Deep Generative Models in the Real-World: An Open Challenge from Medical Imaging
Xiaoran Chen, Nick Pawlowski, Martin Rajchl +2
Recent advances in deep learning led to novel generative modeling techniques that achieve unprecedented quality in generated samples and performance in learning complex distributio…
NeuroNet: Fast and Robust Reproduction of Multiple Brain Image Segmentation Pipelines
Martin Rajchl, Nick Pawlowski, Daniel Rueckert +2
NeuroNet is a deep convolutional neural network mimicking multiple popular and state-of-the-art brain segmentation tools including FSL, SPM, and MALPEM. The network is trained on 5…
DLTK: State of the Art Reference Implementations for Deep Learning on Medical Images
Nick Pawlowski, Sofia Ira Ktena, Matthew C. H. Lee +4
We present DLTK, a toolkit providing baseline implementations for efficient experimentation with deep learning methods on biomedical images. It builds on top of TensorFlow and its…
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…
Implicit Weight Uncertainty in Neural Networks
Nick Pawlowski, Andrew Brock, Matthew C. H. Lee +2
Modern neural networks tend to be overconfident on unseen, noisy or incorrectly labelled data and do not produce meaningful uncertainty measures. Bayesian deep learning aims to add…
Automated cardiovascular magnetic resonance image analysis with fully convolutional networks
Wenjia Bai, Matthew Sinclair, Giacomo Tarroni +20
Cardiovascular magnetic resonance (CMR) imaging is a standard imaging modality for assessing cardiovascular diseases (CVDs), the leading cause of death globally. CMR enables accura…
Employing Weak Annotations for Medical Image Analysis Problems
Martin Rajchl, Lisa M. Koch, Christian Ledig +4
To efficiently establish training databases for machine learning methods, collaborative and crowdsourcing platforms have been investigated to collectively tackle the annotation eff…
Distance Metric Learning using Graph Convolutional Networks: Application to Functional Brain Networks
Sofia Ira Ktena, Sarah Parisot, Enzo Ferrante +4
Evaluating similarity between graphs is of major importance in several computer vision and pattern recognition problems, where graph representations are often used to model objects…
PVR: Patch-to-Volume Reconstruction for Large Area Motion Correction of Fetal MRI
Amir Alansary, Bernhard Kainz, Martin Rajchl +8
In this paper we present a novel method for the correction of motion artifacts that are present in fetal Magnetic Resonance Imaging (MRI) scans of the whole uterus. Contrary to cur…
DeepCut: Object Segmentation from Bounding Box Annotations using Convolutional Neural Networks
Martin Rajchl, Matthew C. H. Lee, Ozan Oktay +8
In this paper, we propose DeepCut, a method to obtain pixelwise object segmentations given an image dataset labelled with bounding box annotations. It extends the approach of the w…
Learning under Distributed Weak Supervision
Martin Rajchl, Matthew C. H. Lee, Franklin Schrans +7
The availability of training data for supervision is a frequently encountered bottleneck of medical image analysis methods. While typically established by a clinical expert rater,…
Multi-Atlas Segmentation using Partially Annotated Data: Methods and Annotation Strategies
Lisa M. Koch, Martin Rajchl, Wenjia Bai +5
Multi-atlas segmentation is a widely used tool in medical image analysis, providing robust and accurate results by learning from annotated atlas datasets. However, the availability…
A Proximal Bregman Projection Approach to Continuous Max-Flow Problems Using Entropic Distances
John S. H. Baxter, Martin Rajchl, Jing Yuan +1
One issue limiting the adaption of large-scale multi-region segmentation is the sometimes prohibitive memory requirements. This is especially troubling considering advances in mass…
A Continuous Max-Flow Approach to Multi-Labeling Problems under Arbitrary Region Regularization
John S. H. Baxter, Martin Rajchl, Jing Yuan +1
The incorporation of region regularization into max-flow segmentation has traditionally focused on ordering and part-whole relationships. A side effect of the development of such m…
RANCOR: Non-Linear Image Registration with Total Variation Regularization
Martin Rajchl, John S. H. Baxter, Wu Qiu +4
Optimization techniques have been widely used in deformable registration, allowing for the incorporation of similarity metrics with regularization mechanisms. These regularization…
A Continuous Max-Flow Approach to General Hierarchical Multi-Labeling Problems
John S. H. Baxter, Martin Rajchl, Jing Yuan +1
Multi-region segmentation algorithms often have the onus of incorporating complex anatomical knowledge representing spatial or geometric relationships between objects, and general-…