Search Results for author: Bernhard Kainz

Found 94 papers, 47 papers with code

Whole-examination AI estimation of fetal biometrics from 20-week ultrasound scans

no code implementations2 Jan 2024 Lorenzo Venturini, Samuel Budd, Alfonso Farruggia, Robert Wright, Jacqueline Matthew, Thomas G. Day, Bernhard Kainz, Reza Razavi, Jo V. Hajnal

We use a Bayesian method to estimate the true value of each biometric from a large number of measurements and probabilistically reject outliers.

Anatomy

Stochastic Vision Transformers with Wasserstein Distance-Aware Attention

no code implementations30 Nov 2023 Franciskus Xaverius Erick, Mina Rezaei, Johanna Paula Müller, Bernhard Kainz

Motivated by this, we introduce a new stochastic vision transformer that integrates uncertainty and distance awareness into self-supervised learning (SSL) pipelines.

Out-of-Distribution Detection Self-Supervised Learning +1

Exploring the Hyperparameter Space of Image Diffusion Models for Echocardiogram Generation

no code implementations2 Nov 2023 Hadrien Reynaud, Bernhard Kainz

This work presents an extensive hyperparameter search on Image Diffusion Models for Echocardiogram generation.

Video Generation

Whole Slide Multiple Instance Learning for Predicting Axillary Lymph Node Metastasis

1 code implementation6 Oct 2023 Glejdis Shkëmbi, Johanna P. Müller, Zhe Li, Katharina Breininger, Peter Schüffler, Bernhard Kainz

Breast cancer is a major concern for women's health globally, with axillary lymph node (ALN) metastasis identification being critical for prognosis evaluation and treatment guidance.

Data Augmentation Multiple Instance Learning +1

Sculpting Efficiency: Pruning Medical Imaging Models for On-Device Inference

no code implementations10 Sep 2023 Sudarshan Sreeram, Bernhard Kainz

Leveraging ML advancements to augment healthcare systems can improve patient outcomes.

Conditional Temporal Attention Networks for Neonatal Cortical Surface Reconstruction

1 code implementation21 Jul 2023 Qiang Ma, Liu Li, Vanessa Kyriakopoulou, Joseph Hajnal, Emma C. Robinson, Bernhard Kainz, Daniel Rueckert

The importance of each SVF, which is estimated by learned attention maps, is conditioned on the age of the neonates and varies with the time step of integration.

Surface Reconstruction

Many tasks make light work: Learning to localise medical anomalies from multiple synthetic tasks

1 code implementation3 Jul 2023 Matthew Baugh, Jeremy Tan, Johanna P. Müller, Mischa Dombrowski, James Batten, Bernhard Kainz

There is a growing interest in single-class modelling and out-of-distribution detection as fully supervised machine learning models cannot reliably identify classes not included in their training.

Out-of-Distribution Detection Self-Supervised Learning

Zero-Shot Anomaly Detection with Pre-trained Segmentation Models

no code implementations15 Jun 2023 Matthew Baugh, James Batten, Johanna P. Müller, Bernhard Kainz

This technical report outlines our submission to the zero-shot track of the Visual Anomaly and Novelty Detection (VAND) 2023 Challenge.

Anomaly Detection Instance Segmentation +5

Quantifying Sample Anonymity in Score-Based Generative Models with Adversarial Fingerprinting

no code implementations2 Jun 2023 Mischa Dombrowski, Bernhard Kainz

Recent advances in score-based generative models have led to a huge spike in the development of downstream applications using generative models ranging from data augmentation over image and video generation to anomaly detection.

Anomaly Detection Data Augmentation +2

Trade-offs in Fine-tuned Diffusion Models Between Accuracy and Interpretability

1 code implementation31 Mar 2023 Mischa Dombrowski, Hadrien Reynaud, Johanna P. Müller, Matthew Baugh, Bernhard Kainz

Recent advancements in diffusion models have significantly impacted the trajectory of generative machine learning research, with many adopting the strategy of fine-tuning pre-trained models using domain-specific text-to-image datasets.

Conditional Image Generation Object Localization +1

Feature-Conditioned Cascaded Video Diffusion Models for Precise Echocardiogram Synthesis

1 code implementation22 Mar 2023 Hadrien Reynaud, Mengyun Qiao, Mischa Dombrowski, Thomas Day, Reza Razavi, Alberto Gomez, Paul Leeson, Bernhard Kainz

So far, video generation has only been possible by providing input data that is as rich as the output data, e. g., image sequence plus conditioning in, video out.

Image Generation Video Generation

Understanding metric-related pitfalls in image analysis validation

no code implementations3 Feb 2023 Annika Reinke, Minu D. Tizabi, Michael Baumgartner, Matthias Eisenmann, Doreen Heckmann-Nötzel, A. Emre Kavur, Tim Rädsch, Carole H. Sudre, Laura Acion, Michela Antonelli, Tal Arbel, Spyridon Bakas, Arriel Benis, Matthew Blaschko, Florian Buettner, M. Jorge Cardoso, Veronika Cheplygina, Jianxu Chen, Evangelia Christodoulou, Beth A. Cimini, Gary S. Collins, Keyvan Farahani, Luciana Ferrer, Adrian Galdran, Bram van Ginneken, Ben Glocker, Patrick Godau, Robert Haase, Daniel A. Hashimoto, Michael M. Hoffman, Merel Huisman, Fabian Isensee, Pierre Jannin, Charles E. Kahn, Dagmar Kainmueller, Bernhard Kainz, Alexandros Karargyris, Alan Karthikesalingam, Hannes Kenngott, Jens Kleesiek, Florian Kofler, Thijs Kooi, Annette Kopp-Schneider, 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, Karel G. M. Moons, Henning Müller, Brennan Nichyporuk, Felix Nickel, Jens Petersen, Susanne M. Rafelski, Nasir Rajpoot, Mauricio Reyes, Michael A. Riegler, Nicola Rieke, Julio Saez-Rodriguez, Clara I. Sánchez, Shravya Shetty, Maarten van Smeden, Ronald M. Summers, Abdel A. Taha, Aleksei Tiulpin, Sotirios A. Tsaftaris, Ben van Calster, Gaël Varoquaux, Manuel Wiesenfarth, Ziv R. Yaniv, Paul F. Jäger, Lena Maier-Hein

Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice.

Self-Supervised 3D Human Pose Estimation in Static Video Via Neural Rendering

no code implementations10 Oct 2022 Luca Schmidtke, Benjamin Hou, Athanasios Vlontzos, Bernhard Kainz

Inferring 3D human pose from 2D images is a challenging and long-standing problem in the field of computer vision with many applications including motion capture, virtual reality, surveillance or gait analysis for sports and medicine.

3D Human Pose Estimation Neural Rendering

nnOOD: A Framework for Benchmarking Self-supervised Anomaly Localisation Methods

1 code implementation2 Sep 2022 Matthew Baugh, Jeremy Tan, Athanasios Vlontzos, Johanna P. Müller, Bernhard Kainz

It is also difficult to assess whether a task generalises well for universal anomaly detection, as they are often only tested on a limited range of anomalies.

Anomaly Detection Benchmarking

Improved post-hoc probability calibration for out-of-domain MRI segmentation

1 code implementation4 Aug 2022 Cheng Ouyang, Shuo Wang, Chen Chen, Zeju Li, Wenjia Bai, Bernhard Kainz, Daniel Rueckert

In image segmentation, well-calibrated probabilities allow radiologists to identify regions where model-predicted segmentations are unreliable.

Image Segmentation MRI segmentation +2

Placenta Segmentation in Ultrasound Imaging: Addressing Sources of Uncertainty and Limited Field-of-View

1 code implementation29 Jun 2022 Veronika A. Zimmer, Alberto Gomez, Emily Skelton, Robert Wright, Gavin Wheeler, Shujie Deng, Nooshin Ghavami, Karen Lloyd, Jacqueline Matthew, Bernhard Kainz, Daniel Rueckert, Joseph V. Hajnal, Julia A. Schnabel

Automatic segmentation of the placenta in fetal ultrasound (US) is challenging due to the (i) high diversity of placenta appearance, (ii) the restricted quality in US resulting in highly variable reference annotations, and (iii) the limited field-of-view of US prohibiting whole placenta assessment at late gestation.

Image Segmentation Multi-Task Learning +3

A Review of Causality for Learning Algorithms in Medical Image Analysis

no code implementations11 Jun 2022 Athanasios Vlontzos, Daniel Rueckert, Bernhard Kainz

Medical image analysis is a vibrant research area that offers doctors and medical practitioners invaluable insight and the ability to accurately diagnose and monitor disease.

BIG-bench Machine Learning Translation

Is More Data All You Need? A Causal Exploration

no code implementations6 Jun 2022 Athanasios Vlontzos, Hadrien Reynaud, Bernhard Kainz

Curating a large scale medical imaging dataset for machine learning applications is both time consuming and expensive.

BIG-bench Machine Learning Image Classification

Metrics reloaded: Recommendations for image analysis validation

1 code implementation3 Jun 2022 Lena Maier-Hein, Annika Reinke, Patrick Godau, Minu D. Tizabi, Florian Buettner, Evangelia Christodoulou, Ben Glocker, Fabian Isensee, Jens Kleesiek, Michal Kozubek, Mauricio Reyes, Michael A. Riegler, Manuel Wiesenfarth, A. Emre Kavur, Carole H. Sudre, Michael Baumgartner, Matthias Eisenmann, Doreen Heckmann-Nötzel, Tim Rädsch, Laura Acion, Michela Antonelli, Tal Arbel, Spyridon Bakas, Arriel Benis, Matthew Blaschko, M. Jorge Cardoso, Veronika Cheplygina, Beth A. Cimini, Gary S. Collins, Keyvan Farahani, Luciana Ferrer, Adrian Galdran, Bram van Ginneken, Robert Haase, Daniel A. Hashimoto, Michael M. Hoffman, Merel Huisman, Pierre Jannin, Charles E. Kahn, Dagmar Kainmueller, Bernhard Kainz, Alexandros Karargyris, Alan Karthikesalingam, Hannes Kenngott, Florian Kofler, Annette Kopp-Schneider, Anna Kreshuk, Tahsin Kurc, Bennett A. Landman, Geert Litjens, Amin Madani, Klaus Maier-Hein, Anne L. Martel, Peter Mattson, Erik Meijering, Bjoern Menze, Karel G. M. Moons, Henning Müller, Brennan Nichyporuk, Felix Nickel, Jens Petersen, Nasir Rajpoot, Nicola Rieke, Julio Saez-Rodriguez, Clara I. Sánchez, Shravya Shetty, Maarten van Smeden, Ronald M. Summers, Abdel A. Taha, Aleksei Tiulpin, Sotirios A. Tsaftaris, Ben van Calster, Gaël Varoquaux, Paul F. Jäger

The framework was developed in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint - a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), data set and algorithm output.

Instance Segmentation object-detection +2

D'ARTAGNAN: Counterfactual Video Generation

1 code implementation3 Jun 2022 Hadrien Reynaud, Athanasios Vlontzos, Mischa Dombrowski, Ciarán Lee, Arian Beqiri, Paul Leeson, Bernhard Kainz

Causally-enabled machine learning frameworks could help clinicians to identify the best course of treatments by answering counterfactual questions.

Anatomy counterfactual +2

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

A Variational Bayesian Method for Similarity Learning in Non-Rigid Image Registration

1 code implementation CVPR 2022 Daniel Grzech, Mohammad Farid Azampour, Ben Glocker, Julia Schnabel, Nassir Navab, Bernhard Kainz, Loïc le Folgoc

We propose a novel variational Bayesian formulation for diffeomorphic non-rigid registration of medical images, which learns in an unsupervised way a data-specific similarity metric.

Image Registration

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 +4

Natural Synthetic Anomalies for Self-Supervised Anomaly Detection and Localization

2 code implementations30 Sep 2021 Hannah M. Schlüter, Jeremy Tan, Benjamin Hou, Bernhard Kainz

We introduce a simple and intuitive self-supervision task, Natural Synthetic Anomalies (NSA), for training an end-to-end model for anomaly detection and localization using only normal training data.

Data Augmentation Out-of-Distribution Detection +2

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 Counterfactual Inference +2

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

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.

reinforcement-learning Reinforcement Learning (RL) +1

Unsupervised Human Pose Estimation through Transforming Shape Templates

2 code implementations 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 +1

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, Matthew Blaschko, Florian Buettner, M. Jorge Cardoso, Jianxu Chen, Veronika Cheplygina, Evangelia Christodoulou, Beth Cimini, Gary S. Collins, Sandy Engelhardt, Keyvan Farahani, Luciana Ferrer, Adrian Galdran, Bram van Ginneken, Ben Glocker, Patrick Godau, Robert Haase, Fred Hamprecht, Daniel A. Hashimoto, Doreen Heckmann-Nötzel, Peter Hirsch, Michael M. Hoffman, Merel Huisman, Fabian Isensee, Pierre Jannin, Charles E. Kahn, Dagmar Kainmueller, Bernhard Kainz, Alexandros Karargyris, Alan Karthikesalingam, A. Emre Kavur, Hannes Kenngott, Jens Kleesiek, Andreas Kleppe, Sven Kohler, Florian Kofler, Annette Kopp-Schneider, 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, Brennan Nichyporuk, Felix Nickel, M. Alican Noyan, Jens Petersen, Gorkem Polat, Susanne M. Rafelski, Nasir Rajpoot, Mauricio Reyes, Nicola Rieke, Michael Riegler, Hassan Rivaz, Julio Saez-Rodriguez, Clara I. Sánchez, Julien Schroeter, Anindo Saha, M. Alper Selver, Lalith Sharan, Shravya Shetty, Maarten van Smeden, Bram Stieltjes, Ronald M. Summers, Abdel A. Taha, Aleksei Tiulpin, Sotirios A. Tsaftaris, Ben van Calster, Gaël Varoquaux, Manuel Wiesenfarth, Ziv R. Yaniv, 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 +2

Topological Information Retrieval with Dilation-Invariant Bottleneck Comparative Measures

1 code implementation4 Apr 2021 Yueqi Cao, Athanasios Vlontzos, 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 Retrieval +1

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.

Anatomy

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.

regression

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.

Future prediction Image Generation

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 Reinforcement Learning (RL) +1

Geomstats: A Python Package for Riemannian Geometry in Machine Learning

1 code implementation ICLR 2019 Nina Miolane, Alice Le Brigant, Johan Mathe, Benjamin Hou, Nicolas Guigui, Yann Thanwerdas, Stefan Heyder, Olivier Peltre, Niklas Koep, Hadi Zaatiti, Hatem Hajri, Yann Cabanes, Thomas Gerald, Paul Chauchat, Christian Shewmake, Bernhard Kainz, Claire Donnat, Susan Holmes, Xavier Pennec

We introduce Geomstats, an open-source Python toolbox for computations and statistics on nonlinear manifolds, such as hyperbolic spaces, spaces of symmetric positive definite matrices, Lie groups of transformations, and many more.

BIG-bench Machine Learning Clustering +2

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.

Graphics

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.

Anatomy

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.

Anatomy 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.

Anatomy Conditional Image Generation +2

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.

Anatomy Disentanglement +1

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.

Anatomy Multi-agent Reinforcement Learning +2

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.

Computational Efficiency General Classification +2

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.

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.

Image 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 +1

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.

Anatomy Retrieval

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