Search Results for author: Bernhard Kainz

Found 64 papers, 32 papers with code

CortexODE: Learning Cortical Surface Reconstruction by Neural ODEs

1 code implementation16 Feb 2022 Qiang Ma, Liu Li, Emma C. Robinson, Bernhard Kainz, Daniel Rueckert, Amir Alansary

Following the isosurface extraction step, two CortexODE models are trained to deform the initial surface to white matter and pial surfaces respectively.

Surface Reconstruction

Cross Modality 3D Navigation Using Reinforcement Learning and Neural Style Transfer

1 code implementation5 Nov 2021 Cesare Magnetti, Hadrien Reynaud, Bernhard Kainz

This paper presents the use of Multi-Agent Reinforcement Learning (MARL) to perform navigation in 3D anatomical volumes from medical imaging.

Computed Tomography (CT) Multi-agent Reinforcement Learning +3

PialNN: A Fast Deep Learning Framework for Cortical Pial Surface Reconstruction

1 code implementation6 Sep 2021 Qiang Ma, Emma C. Robinson, Bernhard Kainz, Daniel Rueckert, Amir Alansary

Traditional cortical surface reconstruction is time consuming and limited by the resolution of brain Magnetic Resonance Imaging (MRI).

Surface Reconstruction

Estimating Categorical Counterfactuals via Deep Twin Networks

no code implementations4 Sep 2021 Athanasios Vlontzos, Bernhard Kainz, Ciaran M. Gilligan-Lee

To learn causal mechanisms satisfying these constraints, and perform counterfactual inference with them, we introduce deep twin networks.

Counterfactual Inference

Contrastive Learning for View Classification of Echocardiograms

no code implementations6 Aug 2021 Agisilaos Chartsias, Shan Gao, Angela Mumith, Jorge Oliveira, Kanwal Bhatia, Bernhard Kainz, Arian Beqiri

Analysis of cardiac ultrasound images is commonly performed in routine clinical practice for quantification of cardiac function.

Classification Contrastive Learning

Can non-specialists provide high quality gold standard labels in challenging modalities?

no code implementations30 Jul 2021 Samuel Budd, Thomas Day, John Simpson, Karen Lloyd, Jacqueline Matthew, Emily Skelton, Reza Razavi, Bernhard Kainz

We study the time and cost implications of using novice annotators, the raw performance of novice annotators compared to gold-standard expert annotators, and the downstream effects on a trained Deep Learning segmentation model's performance for detecting a specific congenital heart disease (hypoplastic left heart syndrome) in fetal ultrasound imaging.

Detecting Outliers with Poisson Image Interpolation

1 code implementation6 Jul 2021 Jeremy Tan, Benjamin Hou, Thomas Day, John Simpson, Daniel Rueckert, Bernhard Kainz

We propose an alternative to image reconstruction-based and image embedding-based methods and propose a new self-supervised method to tackle pathological anomaly detection.

Anomaly Detection Image Reconstruction

Ultrasound Video Transformers for Cardiac Ejection Fraction Estimation

1 code implementation2 Jul 2021 Hadrien Reynaud, Athanasios Vlontzos, Benjamin Hou, Arian Beqiri, Paul Leeson, Bernhard Kainz

We achieve an average frame distance of 3. 36 frames for the ES and 7. 17 frames for the ED on videos of arbitrary length.

Frame Token Classification

Video Summarization through Reinforcement Learning with a 3D Spatio-Temporal U-Net

no code implementations19 Jun 2021 Tianrui Liu, Qingjie Meng, Jun-Jie Huang, Athanasios Vlontzos, Daniel Rueckert, Bernhard Kainz

Intelligent video summarization algorithms allow to quickly convey the most relevant information in videos through the identification of the most essential and explanatory content while removing redundant video frames.

Frame reinforcement-learning +1

Unsupervised Human Pose Estimation through Transforming Shape Templates

1 code implementation CVPR 2021 Luca Schmidtke, Athanasios Vlontzos, Simon Ellershaw, Anna Lukens, Tomoki Arichi, Bernhard Kainz

Human pose estimation is a major computer vision problem with applications ranging from augmented reality and video capture to surveillance and movement tracking.

Pose Estimation Template Matching

Common Limitations of Image Processing Metrics: A Picture Story

1 code implementation12 Apr 2021 Annika Reinke, Minu D. Tizabi, Carole H. Sudre, Matthias Eisenmann, Tim Rädsch, Michael Baumgartner, Laura Acion, Michela Antonelli, Tal Arbel, Spyridon Bakas, Peter Bankhead, Arriel Benis, M. Jorge Cardoso, Veronika Cheplygina, Beth Cimini, Gary S. Collins, Keyvan Farahani, Ben Glocker, Patrick Godau, Fred Hamprecht, Daniel A. Hashimoto, Doreen Heckmann-Nötzel, Michael M. Hoffmann, Merel Huisman, Fabian Isensee, Pierre Jannin, Charles E. Kahn, Alexandros Karargyris, Alan Karthikesalingam, Bernhard Kainz, Emre Kavur, Hannes Kenngott, Jens Kleesiek, Thijs Kooi, Michal Kozubek, Anna Kreshuk, Tahsin Kurc, Bennett A. Landman, Geert Litjens, Amin Madani, Klaus Maier-Hein, Anne L. Martel, Peter Mattson, Erik Meijering, Bjoern Menze, David Moher, Karel G. M. Moons, Henning Müller, Felix Nickel, Jens Petersen, Gorkem Polat, Nasir Rajpoot, Mauricio Reyes, Nicola Rieke, Michael Riegler, Hassan Rivaz, Julio Saez-Rodriguez, Clarisa Sanchez Gutierrez, Julien Schroeter, Anindo Saha, Shravya Shetty, Bram Stieltjes, Ronald M. Summers, Abdel A. Taha, Sotirios A. Tsaftaris, Bram van Ginneken, Gaël Varoquaux, Manuel Wiesenfarth, Ziv R. Yaniv, Annette Kopp-Schneider, Paul Jäger, Lena Maier-Hein

While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation.

Instance Segmentation Object Detection +1

Topological Information Retrieval with Dilation-Invariant Bottleneck Comparative Measures

1 code implementation4 Apr 2021 Athanasios Vlontzos, Yueqi Cao, Luca Schmidtke, Bernhard Kainz, Anthea Monod

Appropriately representing elements in a database so that queries may be accurately matched is a central task in information retrieval; recently, this has been achieved by embedding the graphical structure of the database into a manifold in a hierarchy-preserving manner using a variety of metrics.

Information Retrieval Topological Data Analysis

Detecting Outliers with Foreign Patch Interpolation

1 code implementation9 Nov 2020 Jeremy Tan, Benjamin Hou, James Batten, Huaqi Qiu, Bernhard Kainz

A wide residual encoder decoder is trained to give a pixel-wise prediction of the patch and its interpolation factor.

Mutual Information-based Disentangled Neural Networks for Classifying Unseen Categories in Different Domains: Application to Fetal Ultrasound Imaging

no code implementations30 Oct 2020 Qingjie Meng, Jacqueline Matthew, Veronika A. Zimmer, Alberto Gomez, David F. A. Lloyd, Daniel Rueckert, Bernhard Kainz

To address this problem, we propose Mutual Information-based Disentangled Neural Networks (MIDNet), which extract generalizable categorical features to transfer knowledge to unseen categories in a target domain.

Image Classification

Surface Agnostic Metrics for Cortical Volume Segmentation and Regression

no code implementations4 Oct 2020 Samuel Budd, Prachi Patkee, Ana Baburamani, Mary Rutherford, Emma C. Robinson, Bernhard Kainz

The cerebral cortex performs higher-order brain functions and is thus implicated in a range of cognitive disorders.

Causal Future Prediction in a Minkowski Space-Time

no code implementations20 Aug 2020 Athanasios Vlontzos, Henrique Bergallo Rocha, Daniel Rueckert, Bernhard Kainz

In this paper we propose a novel theoretical framework to perform causal future prediction by embedding spatiotemporal information on a Minkowski space-time.

Frame Future prediction +1

Unsupervised Cross-domain Image Classification by Distance Metric Guided Feature Alignment

1 code implementation19 Aug 2020 Qingjie Meng, Daniel Rueckert, Bernhard Kainz

The proposed MetFA method explicitly and directly learns the latent representation without using domain adversarial training.

General Classification Image Classification +1

Projective Latent Interventions for Understanding and Fine-tuning Classifiers

1 code implementation23 Jun 2020 Andreas Hinterreiter, Marc Streit, Bernhard Kainz

We present Projective Latent Interventions (PLIs), a technique for retraining classifiers by back-propagating manual changes made to low-dimensional embeddings of the latent space.

General Classification

3D Probabilistic Segmentation and Volumetry from 2D projection images

no code implementations23 Jun 2020 Athanasios Vlontzos, Samuel Budd, Benjamin Hou, Daniel Rueckert, Bernhard Kainz

X-Ray imaging is quick, cheap and useful for front-line care assessment and intra-operative real-time imaging (e. g., C-Arm Fluoroscopy).

Ultrasound Video Summarization using Deep Reinforcement Learning

1 code implementation19 May 2020 Tianrui Liu, Qingjie Meng, Athanasios Vlontzos, Jeremy Tan, Daniel Rueckert, Bernhard Kainz

We show that our method is superior to alternative video summarization methods and that it preserves essential information required by clinical diagnostic standards.

reinforcement-learning Video Summarization

A Survey on Active Learning and Human-in-the-Loop Deep Learning for Medical Image Analysis

no code implementations7 Oct 2019 Samuel Budd, Emma C. Robinson, Bernhard Kainz

Fully automatic deep learning has become the state-of-the-art technique for many tasks including image acquisition, analysis and interpretation, and for the extraction of clinically useful information for computer-aided detection, diagnosis, treatment planning, intervention and therapy.

Active Learning

One Shot Radiance: Global Illumination Using Convolutional Autoencoders

no code implementations6 Oct 2019 Giulio Jiang, Bernhard Kainz

Rendering realistic images with Global Illumination (GI) is a computationally demanding task and often requires dedicated hardware for feasible runtime.


Automatic Detection of Bowel Disease with Residual Networks

1 code implementation31 Aug 2019 Robert Holland, Uday Patel, Phillip Lung, Elisa Chotzoglou, Bernhard Kainz

Crohn's disease, one of two inflammatory bowel diseases (IBD), affects 200, 000 people in the UK alone, or roughly one in every 500.

Semi-supervised Learning of Fetal Anatomy from Ultrasound

no code implementations30 Aug 2019 Jeremy Tan, Anselm Au, Qingjie Meng, Bernhard Kainz

Semi-supervised learning methods have achieved excellent performance on standard benchmark datasets using very few labelled images.

General Classification

Flexible Conditional Image Generation of Missing Data with Learned Mental Maps

no code implementations29 Aug 2019 Benjamin Hou, Athanasios Vlontzos, Amir Alansary, Daniel Rueckert, Bernhard Kainz

Real-world settings often do not allow acquisition of high-resolution volumetric images for accurate morphological assessment and diagnostic.

Conditional Image Generation Decision Making +1

Representation Disentanglement for Multi-task Learning with application to Fetal Ultrasound

1 code implementation21 Aug 2019 Qingjie Meng, Nick Pawlowski, Daniel Rueckert, Bernhard Kainz

These entangled image properties lead to a semantically redundant feature encoding for the relevant task and thus lead to poor generalization of deep learning algorithms.

Disentanglement Multi-Task Learning

Multiple Landmark Detection using Multi-Agent Reinforcement Learning

1 code implementation30 Jun 2019 Athanasios Vlontzos, Amir Alansary, Konstantinos Kamnitsas, Daniel Rueckert, Bernhard Kainz

We compare our approach with state-of-the-art architectures and achieve significantly better accuracy by reducing the detection error by 50%, while requiring fewer computational resources and time to train compared to the naive approach of training K agents separately.

Multi-agent Reinforcement Learning reinforcement-learning

Morpho-MNIST: Quantitative Assessment and Diagnostics for Representation Learning

1 code implementation ICLR 2019 Daniel C. Castro, Jeremy Tan, Bernhard Kainz, Ender Konukoglu, Ben Glocker

Revealing latent structure in data is an active field of research, having introduced exciting technologies such as variational autoencoders and adversarial networks, and is essential to push machine learning towards unsupervised knowledge discovery.

Domain Adaptation Outlier Detection +1

Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images

2 code implementations22 Aug 2018 Jo Schlemper, Ozan Oktay, Michiel Schaap, Mattias Heinrich, Bernhard Kainz, Ben Glocker, Daniel Rueckert

AGs can be easily integrated into standard CNN models such as VGG or U-Net architectures with minimal computational overhead while increasing the model sensitivity and prediction accuracy.

General Classification Image Classification

Fast Multiple Landmark Localisation Using a Patch-based Iterative Network

1 code implementation18 Jun 2018 Yuanwei Li, Amir Alansary, Juan J. Cerrolaza, Bishesh Khanal, Matthew Sinclair, Jacqueline Matthew, Chandni Gupta, Caroline Knight, Bernhard Kainz, Daniel Rueckert

PIN is computationally efficient since the inference stage only selectively samples a small number of patches in an iterative fashion rather than a dense sampling at every location in the volume.

Multi-Task Learning

Attention-Gated Networks for Improving Ultrasound Scan Plane Detection

6 code implementations15 Apr 2018 Jo Schlemper, Ozan Oktay, Liang Chen, Jacqueline Matthew, Caroline Knight, Bernhard Kainz, Ben Glocker, Daniel Rueckert

We show that, when the base network has a high capacity, the incorporated attention mechanism can provide efficient object localisation while improving the overall performance.

Attention U-Net: Learning Where to Look for the Pancreas

27 code implementations11 Apr 2018 Ozan Oktay, Jo Schlemper, Loic Le Folgoc, Matthew Lee, Mattias Heinrich, Kazunari Misawa, Kensaku Mori, Steven McDonagh, Nils Y. Hammerla, Bernhard Kainz, Ben Glocker, Daniel Rueckert

We propose a novel attention gate (AG) model for medical imaging that automatically learns to focus on target structures of varying shapes and sizes.

Brain Tumor Segmentation Pancreas Segmentation +1

Efficient Image Evidence Analysis of CNN Classification Results

no code implementations5 Jan 2018 Keyang Zhou, Bernhard Kainz

We believe that our work makes network introspection more feasible for debugging and understanding deep convolutional networks.

Classification General Classification +1

DLTK: State of the Art Reference Implementations for Deep Learning on Medical Images

1 code implementation18 Nov 2017 Nick Pawlowski, Sofia Ira Ktena, Matthew C. H. Lee, Bernhard Kainz, Daniel Rueckert, Ben Glocker, Martin Rajchl

We present DLTK, a toolkit providing baseline implementations for efficient experimentation with deep learning methods on biomedical images.

Semantic Segmentation

Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation

1 code implementation4 Nov 2017 Konstantinos Kamnitsas, Wenjia Bai, Enzo Ferrante, Steven McDonagh, Matthew Sinclair, Nick Pawlowski, Martin Rajchl, Matthew Lee, Bernhard Kainz, Daniel Rueckert, Ben Glocker

Deep learning approaches such as convolutional neural nets have consistently outperformed previous methods on challenging tasks such as dense, semantic segmentation.

Semantic Segmentation

Automated cardiovascular magnetic resonance image analysis with fully convolutional networks

1 code implementation25 Oct 2017 Wenjia Bai, Matthew Sinclair, Giacomo Tarroni, Ozan Oktay, Martin Rajchl, Ghislain Vaillant, Aaron M. Lee, Nay Aung, Elena Lukaschuk, Mihir M. Sanghvi, Filip Zemrak, Kenneth Fung, Jose Miguel Paiva, Valentina Carapella, Young Jin Kim, Hideaki Suzuki, Bernhard Kainz, Paul M. Matthews, Steffen E. Petersen, Stefan K. Piechnik, Stefan Neubauer, Ben Glocker, Daniel Rueckert

By combining FCN with a large-scale annotated dataset, the proposed automated method achieves a high performance on par with human experts in segmenting the LV and RV on short-axis CMR images and the left atrium (LA) and right atrium (RA) on long-axis CMR images.

3D Reconstruction in Canonical Co-ordinate Space from Arbitrarily Oriented 2D Images

no code implementations19 Sep 2017 Benjamin Hou, Bishesh Khanal, Amir Alansary, Steven McDonagh, Alice Davidson, Mary Rutherford, Jo V. Hajnal, Daniel Rueckert, Ben Glocker, Bernhard Kainz

We extensively evaluate the effectiveness of our approach quantitatively on simulated Magnetic Resonance Imaging (MRI), fetal brain imagery with synthetic motion and further demonstrate qualitative results on real fetal MRI data where our method is integrated into a full reconstruction and motion compensation pipeline.

3D Reconstruction Image Reconstruction +2

Predicting Slice-to-Volume Transformation in Presence of Arbitrary Subject Motion

1 code implementation28 Feb 2017 Benjamin Hou, Amir Alansary, Steven McDonagh, Alice Davidson, Mary Rutherford, Jo V. Hajnal, Daniel Rueckert, Ben Glocker, Bernhard Kainz

Our approach is attractive in challenging imaging scenarios, where significant subject motion complicates reconstruction performance of 3D volumes from 2D slice data.

Image Registration Motion Compensation

SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound

2 code implementations16 Dec 2016 Christian F. Baumgartner, Konstantinos Kamnitsas, Jacqueline Matthew, Tara P. Fletcher, Sandra Smith, Lisa M. Koch, Bernhard Kainz, Daniel Rueckert

In this paper, we propose a novel method based on convolutional neural networks which can automatically detect 13 fetal standard views in freehand 2D ultrasound data as well as provide a localisation of the fetal structures via a bounding box.


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