Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Centre National de la Recherche ScientifiqueFR550 papers
- European Organization for Nuclear ResearchCH528 papers
- University College LondonGB520 papers
- University of CambridgeGB423 papers
- University of OxfordGB419 papers
- The University of TokyoJP352 papers
- Imperial College LondonGB340 papers
- Sorbonne UniversitéFR332 papers
- University of SheffieldGB328 papers
- Université Paris-SaclayFR326 papers
- Sapienza University of RomeIT321 papers
- University of GlasgowGB318 papers
21 papers · 2 filters
Automatic Brain Tumor Segmentation using Convolutional Neural Networks with Test-Time Augmentation
Guotai Wang, Wenqi Li, Sebastien Ourselin +1
Automatic brain tumor segmentation plays an important role for diagnosis, surgical planning and treatment assessment of brain tumors. Deep convolutional neural networks (CNNs) have…
GAN Augmentation: Augmenting Training Data using Generative Adversarial Networks
Christopher Bowles, Liang Chen, Ricardo Guerrero +7
One of the biggest issues facing the use of machine learning in medical imaging is the lack of availability of large, labelled datasets. The annotation of medical images is not onl…
PADDIT: Probabilistic Augmentation of Data using Diffeomorphic Image Transformation
Mauricio Orbes Arteaga, Lauge Sørensen, M. Jorge Cardoso +6
For proper generalization performance of convolutional neural networks (CNNs) in medical image segmentation, the learnt features should be invariant under particular non-linear sha…
A Generative Adversarial Model for Right Ventricle Segmentation
Nicoló Savioli, Miguel Silva Vieira, Pablo Lamata +1
The clinical management of several cardiovascular conditions, such as pulmonary hypertension, require the assessment of the right ventricular (RV) function. This work addresses the…
Elastic Registration of Geodesic Vascular Graphs
Stefano Moriconi, Maria A. Zuluaga, H. Rolf Jager +3
Vascular graphs can embed a number of high-level features, from morphological parameters, to functional biomarkers, and represent an invaluable tool for longitudinal and cross-sect…
Image computing for fibre-bundle endomicroscopy: A review
Antonios Perperidis, Kevin Dhaliwal, Stephen McLaughlin +1
Endomicroscopy is an emerging imaging modality, that facilitates the acquisition of in vivo, in situ optical biopsies, assisting diagnostic and potentially therapeutic intervention…
Automated segmentation on the entire cardiac cycle using a deep learning work-flow
Nicoló Savioli, Miguel Silva Vieira, Pablo Lamata +1
The segmentation of the left ventricle (LV) from CINE MRI images is essential to infer important clinical parameters. Typically, machine learning algorithms for automated LV segmen…
Deep Learning using K-space Based Data Augmentation for Automated Cardiac MR Motion Artefact Detection
Ilkay Oksuz, Bram Ruijsink, Esther Puyol-Anton +6
Quality assessment of medical images is essential for complete automation of image processing pipelines. For large population studies such as the UK Biobank, artefacts such as thos…
Weakly Supervised Localisation for Fetal Ultrasound Images
Nicolas Toussaint, Bishesh Khanal, Matthew Sinclair +4
This paper addresses the task of detecting and localising fetal anatomical regions in 2D ultrasound images, where only image-level labels are present at training, i.e. without any…
Learning associations between clinical information and motion-based descriptors using a large scale MR-derived cardiac motion atlas
Esther Puyol-Anton, Bram Ruijsink, Helene Langet +4
The availability of large scale databases containing imaging and non-imaging data, such as the UK Biobank, represents an opportunity to improve our understanding of healthy and dis…
EchoFusion: Tracking and Reconstruction of Objects in 4D Freehand Ultrasound Imaging without External Trackers
Bishesh Khanal, Alberto Gomez, Nicolas Toussaint +11
Ultrasound (US) is the most widely used fetal imaging technique. However, US images have limited capture range, and suffer from view dependent artefacts such as acoustic shadows. C…
PIMMS: Permutation Invariant Multi-Modal Segmentation
Thomas Varsavsky, Zach Eaton-Rosen, Carole H. Sudre +2
In a research context, image acquisition will often involve a pre-defined static protocol and the data will be of high quality. If we are to build applications that work in hospita…
Temporal Convolution Networks for Real-Time Abdominal Fetal Aorta Analysis with Ultrasound
Nicolo' Savioli, Silvia Visentin, Erich Cosmi +3
The automatic analysis of ultrasound sequences can substantially improve the efficiency of clinical diagnosis. In this work we present our attempt to automate the challenging task…
Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks
Guotai Wang, Wenqi Li, Michael Aertsen +3
Despite the state-of-the-art performance for medical image segmentation, deep convolutional neural networks (CNNs) have rarely provided uncertainty estimations regarding their segm…
Towards safe deep learning: accurately quantifying biomarker uncertainty in neural network predictions
Zach Eaton-Rosen, Felix Bragman, Sotirios Bisdas +2
Automated medical image segmentation, specifically using deep learning, has shown outstanding performance in semantic segmentation tasks. However, these methods rarely quantify the…
Uncertainty in multitask learning: joint representations for probabilistic MR-only radiotherapy planning
Felix J. S. Bragman, Ryutaro Tanno, Zach Eaton-Rosen +6
Multi-task neural network architectures provide a mechanism that jointly integrates information from distinct sources. It is ideal in the context of MR-only radiotherapy planning a…
Standard Plane Detection in 3D Fetal Ultrasound Using an Iterative Transformation Network
Yuanwei Li, Bishesh Khanal, Benjamin Hou +8
Standard scan plane detection in fetal brain ultrasound (US) forms a crucial step in the assessment of fetal development. In clinical settings, this is done by manually manoeuvring…
Fast Multiple Landmark Localisation Using a Patch-based Iterative Network
Yuanwei Li, Amir Alansary, Juan J. Cerrolaza +7
We propose a new Patch-based Iterative Network (PIN) for fast and accurate landmark localisation in 3D medical volumes. PIN utilises a Convolutional Neural Network (CNN) to learn t…
MedGAN: Medical Image Translation using GANs
Karim Armanious, Chenming Jiang, Marc Fischer +4
Image-to-image translation is considered a new frontier in the field of medical image analysis, with numerous potential applications. However, a large portion of recent approaches…
Computing CNN Loss and Gradients for Pose Estimation with Riemannian Geometry
Benjamin Hou, Nina Miolane, Bishesh Khanal +7
Pose estimation, i.e. predicting a 3D rigid transformation with respect to a fixed co-ordinate frame in, SE(3), is an omnipresent problem in medical image analysis with application…
Attention-Gated Networks for Improving Ultrasound Scan Plane Detection
Jo Schlemper, Ozan Oktay, Liang Chen +5
In this work, we apply an attention-gated network to real-time automated scan plane detection for fetal ultrasound screening. Scan plane detection in fetal ultrasound is a challeng…