Search Results for author: Ben Glocker

Found 78 papers, 40 papers with code

Is MC Dropout Bayesian?

no code implementations8 Oct 2021 Loic Le Folgoc, Vasileios Baltatzis, Sujal Desai, Anand Devaraj, Sam Ellis, Octavio E. Martinez Manzanera, Arjun Nair, Huaqi Qiu, Julia Schnabel, Ben Glocker

We question the properties of MC Dropout for approximate inference, as in fact MC Dropout changes the Bayesian model; its predictive posterior assigns $0$ probability to the true model on closed-form benchmarks; the multimodality of its predictive posterior is not a property of the true predictive posterior but a design artefact.

Variational Inference

DeepMCAT: Large-Scale Deep Clustering for Medical Image Categorization

no code implementations30 Sep 2021 Turkay Kart, Wenjia Bai, Ben Glocker, Daniel Rueckert

In recent years, the research landscape of machine learning in medical imaging has changed drastically from supervised to semi-, weakly- or unsupervised methods.

Deep Clustering Image Categorization

Class-Distribution-Aware Calibration for Long-Tailed Visual Recognition

1 code implementation11 Sep 2021 Mobarakol Islam, Lalithkumar Seenivasan, Hongliang Ren, Ben Glocker

In CDA-TS, the scalar temperature value is replaced with the CDA temperature vector encoded with class frequency to compensate for the over-confidence.

Active label cleaning: Improving dataset quality under resource constraints

1 code implementation1 Sep 2021 Melanie Bernhardt, Daniel C. Castro, Ryutaro Tanno, Anton Schwaighofer, Kerem C. Tezcan, Miguel Monteiro, Shruthi Bannur, Matthew Lungren, Aditya Nori, Ben Glocker, Javier Alvarez-Valle, Ozan Oktay

Imperfections in data annotation, known as label noise, are detrimental to the training of machine learning models and have an often-overlooked confounding effect on the assessment of model performance.

The Pitfalls of Sample Selection: A Case Study on Lung Nodule Classification

no code implementations11 Aug 2021 Vasileios Baltatzis, Kyriaki-Margarita Bintsi, Loic Le Folgoc, Octavio E. Martinez Manzanera, Sam Ellis, Arjun Nair, Sujal Desai, Ben Glocker, Julia A. Schnabel

Using publicly available data to determine the performance of methodological contributions is important as it facilitates reproducibility and allows scrutiny of the published results.

Lung Nodule Classification

A Review of Generative Adversarial Networks in Cancer Imaging: New Applications, New Solutions

no code implementations20 Jul 2021 Richard Osuala, Kaisar Kushibar, Lidia Garrucho, Akis Linardos, Zuzanna Szafranowska, Stefan Klein, Ben Glocker, Oliver Diaz, Karim Lekadir

In this review, we assess the potential of GANs to address a number of key challenges of cancer imaging, including data scarcity and imbalance, domain and dataset shifts, data access and privacy, data annotation and quantification, as well as cancer detection, tumour profiling and treatment planning.

Distributional Gaussian Process Layers for Outlier Detection in Image Segmentation

no code implementations28 Apr 2021 Sebastian G. Popescu, David J. Sharp, James H. Cole, Konstantinos Kamnitsas, Ben Glocker

We propose a parameter efficient Bayesian layer for hierarchical convolutional Gaussian Processes that incorporates Gaussian Processes operating in Wasserstein-2 space to reliably propagate uncertainty.

Gaussian Processes Outlier Detection +2

Spatially Varying Label Smoothing: Capturing Uncertainty from Expert Annotations

1 code implementation12 Apr 2021 Mobarakol Islam, Ben Glocker

The task of image segmentation is inherently noisy due to ambiguities regarding the exact location of boundaries between anatomical structures.

Semantic Segmentation

Analyzing Overfitting under Class Imbalance in Neural Networks for Image Segmentation

1 code implementation20 Feb 2021 Zeju Li, Konstantinos Kamnitsas, Ben Glocker

In particular, in image segmentation neural networks may overfit to the foreground samples from small structures, which are often heavily under-represented in the training set, leading to poor generalization.

Data Augmentation Semantic Segmentation

Atlas-ISTN: Joint Segmentation, Registration and Atlas Construction with Image-and-Spatial Transformer Networks

no code implementations18 Dec 2020 Matthew Sinclair, Andreas Schuh, Karl Hahn, Kersten Petersen, Ying Bai, James Batten, Michiel Schaap, Ben Glocker

We propose Atlas-ISTN, a framework that jointly learns segmentation and registration on 2D and 3D image data, and constructs a population-derived atlas in the process.

Image Registration Semantic Segmentation

Hierarchical Gaussian Processes with Wasserstein-2 Kernels

no code implementations28 Oct 2020 Sebastian Popescu, David Sharp, James Cole, Ben Glocker

We investigate the usefulness of Wasserstein-2 kernels in the context of hierarchical Gaussian Processes.

Gaussian Processes Out-of-Distribution Detection

Cranial Implant Design via Virtual Craniectomy with Shape Priors

no code implementations29 Sep 2020 Franco Matzkin, Virginia Newcombe, Ben Glocker, Enzo Ferrante

Our direct estimation method outperforms the baselines provided by the organizers, while the model with shape priors shows superior performance when dealing with out-of-distribution cases.

Self-supervised Skull Reconstruction in Brain CT Images with Decompressive Craniectomy

no code implementations7 Jul 2020 Franco Matzkin, Virginia Newcombe, Susan Stevenson, Aneesh Khetani, Tom Newman, Richard Digby, Andrew Stevens, Ben Glocker, Enzo Ferrante

Decompressive craniectomy (DC) is a common surgical procedure consisting of the removal of a portion of the skull that is performed after incidents such as stroke, traumatic brain injury (TBI) or other events that could result in acute subdural hemorrhage and/or increasing intracranial pressure.

Self-Supervised Learning

Post-DAE: Anatomically Plausible Segmentation via Post-Processing with Denoising Autoencoders

no code implementations24 Jun 2020 Agostina J. Larrazabal, César Martínez, Ben Glocker, Enzo Ferrante

We introduce Post-DAE, a post-processing method based on denoising autoencoders (DAE) to improve the anatomical plausibility of arbitrary biomedical image segmentation algorithms.

Denoising Semantic Segmentation

Deep Generative Model-based Quality Control for Cardiac MRI Segmentation

no code implementations23 Jun 2020 Shuo Wang, Giacomo Tarroni, Chen Qin, Yuanhan Mo, Chengliang Dai, Chen Chen, Ben Glocker, Yike Guo, Daniel Rueckert, Wenjia Bai

Our approach provides a real-time and model-agnostic quality control for cardiac MRI segmentation, which has the potential to be integrated into clinical image analysis workflows.

MRI segmentation

Unpaired Multi-modal Segmentation via Knowledge Distillation

1 code implementation6 Jan 2020 Qi Dou, Quande Liu, Pheng Ann Heng, Ben Glocker

We propose a novel learning scheme for unpaired cross-modality image segmentation, with a highly compact architecture achieving superior segmentation accuracy.

Knowledge Distillation Semantic Segmentation

Causality matters in medical imaging

no code implementations17 Dec 2019 Daniel C. Castro, Ian Walker, Ben Glocker

This article discusses how the language of causality can shed new light on the major challenges in machine learning for medical imaging: 1) data scarcity, which is the limited availability of high-quality annotations, and 2) data mismatch, whereby a trained algorithm may fail to generalize in clinical practice.

Lesion Classification Semantic Segmentation +1

Universal Adversarial Robustness of Texture and Shape-Biased Models

1 code implementation23 Nov 2019 Kenneth T. Co, Luis Muñoz-González, Leslie Kanthan, Ben Glocker, Emil C. Lupu

Increasing shape-bias in deep neural networks has been shown to improve robustness to common corruptions and noise.

Image Classification

Vertebrae Detection and Localization in CT with Two-Stage CNNs and Dense Annotations

1 code implementation14 Oct 2019 James McCouat, Ben Glocker

We propose a new, two-stage approach to the vertebrae centroid detection and localization problem.

Machine Learning with Multi-Site Imaging Data: An Empirical Study on the Impact of Scanner Effects

no code implementations10 Oct 2019 Ben Glocker, Robert Robinson, Daniel C. Castro, Qi Dou, Ender Konukoglu

This is an empirical study to investigate the impact of scanner effects when using machine learning on multi-site neuroimaging data.

Needles in Haystacks: On Classifying Tiny Objects in Large Images

1 code implementation16 Aug 2019 Nick Pawlowski, Suvrat Bhooshan, Nicolas Ballas, Francesco Ciompi, Ben Glocker, Michal Drozdzal

In some important computer vision domains, such as medical or hyperspectral imaging, we care about the classification of tiny objects in large images.

Classification General Classification +2

Overfitting of neural nets under class imbalance: Analysis and improvements for segmentation

1 code implementation25 Jul 2019 Zeju Li, Konstantinos Kamnitsas, Ben Glocker

Overfitting in deep learning has been the focus of a number of recent works, yet its exact impact on the behavior of neural networks is not well understood.

Semantic Segmentation

Is Texture Predictive for Age and Sex in Brain MRI?

1 code implementation25 Jul 2019 Nick Pawlowski, Ben Glocker

Deep learning builds the foundation for many medical image analysis tasks where neuralnetworks are often designed to have a large receptive field to incorporate long spatialdependencies.

Image-and-Spatial Transformer Networks for Structure-Guided Image Registration

1 code implementation22 Jul 2019 Matthew C. H. Lee, Ozan Oktay, Andreas Schuh, Michiel Schaap, Ben Glocker

The goal is to learn a complex function that maps the appearance of input image pairs to parameters of a spatial transformation in order to align corresponding anatomical structures.

Image Registration

Improving RetinaNet for CT Lesion Detection with Dense Masks from Weak RECIST Labels

3 code implementations5 Jun 2019 Martin Zlocha, Qi Dou, Ben Glocker

We propose a highly accurate and efficient one-stage lesion detector, by re-designing a RetinaNet to meet the particular challenges in medical imaging.

Computed Tomography (CT) Region Proposal +1

Medical Imaging with Deep Learning: MIDL 2019 -- Extended Abstract Track

no code implementations21 May 2019 M. Jorge Cardoso, Aasa Feragen, Ben Glocker, Ender Konukoglu, Ipek Oguz, Gozde Unal, Tom Vercauteren

This compendium gathers all the accepted extended abstracts from the Second International Conference on Medical Imaging with Deep Learning (MIDL 2019), held in London, UK, 8-10 July 2019.

Quantitative Error Prediction of Medical Image Registration using Regression Forests

1 code implementation18 May 2019 Hessam Sokooti, Gorkem Saygili, Ben Glocker, Boudewijn P. F. Lelieveldt, Marius Staring

This paper proposes a new automatic method to predict the registration error in a quantitative manner, and is applied to chest CT scans.

Image Registration Medical Image Registration

Graph Convolutional Gaussian Processes

no code implementations14 May 2019 Ian Walker, Ben Glocker

We propose a novel Bayesian nonparametric method to learn translation-invariant relationships on non-Euclidean domains.

Gaussian Processes Superpixel Image Classification +1

PnP-AdaNet: Plug-and-Play Adversarial Domain Adaptation Network with a Benchmark at Cross-modality Cardiac Segmentation

2 code implementations19 Dec 2018 Qi Dou, Cheng Ouyang, Cheng Chen, Hao Chen, Ben Glocker, Xiahai Zhuang, Pheng-Ann Heng

In this paper, we propose the PnPAdaNet (plug-and-play adversarial domain adaptation network) for adapting segmentation networks between different modalities of medical images, e. g., MRI and CT. We propose to tackle the significant domain shift by aligning the feature spaces of source and target domains in an unsupervised manner.

Cardiac Segmentation Domain Adaptation +1

Towards continual learning in medical imaging

no code implementations6 Nov 2018 Chaitanya Baweja, Ben Glocker, Konstantinos Kamnitsas

This work investigates continual learning of two segmentation tasks in brain MRI with neural networks.

Atari Games Continual Learning

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

1 code implementation5 Nov 2018 Spyridon Bakas, Mauricio Reyes, Andras Jakab, Stefan Bauer, Markus Rempfler, Alessandro Crimi, Russell Takeshi Shinohara, Christoph Berger, Sung Min Ha, Martin Rozycki, Marcel Prastawa, Esther Alberts, Jana Lipkova, John Freymann, Justin Kirby, Michel Bilello, Hassan Fathallah-Shaykh, Roland Wiest, Jan Kirschke, Benedikt Wiestler, Rivka Colen, Aikaterini Kotrotsou, Pamela Lamontagne, Daniel Marcus, Mikhail Milchenko, Arash Nazeri, Marc-Andre Weber, Abhishek Mahajan, Ujjwal Baid, Elizabeth Gerstner, Dongjin Kwon, Gagan Acharya, Manu Agarwal, Mahbubul Alam, Alberto Albiol, Antonio Albiol, Francisco J. Albiol, Varghese Alex, Nigel Allinson, Pedro H. A. Amorim, Abhijit Amrutkar, Ganesh Anand, Simon Andermatt, Tal Arbel, Pablo Arbelaez, Aaron Avery, Muneeza Azmat, Pranjal B., W Bai, Subhashis Banerjee, Bill Barth, Thomas Batchelder, Kayhan Batmanghelich, Enzo Battistella, Andrew Beers, Mikhail Belyaev, Martin Bendszus, Eze Benson, Jose Bernal, Halandur Nagaraja Bharath, George Biros, Sotirios Bisdas, James Brown, Mariano Cabezas, Shilei Cao, Jorge M. Cardoso, Eric N Carver, Adrià Casamitjana, Laura Silvana Castillo, Marcel Catà, Philippe Cattin, Albert Cerigues, Vinicius S. Chagas, Siddhartha Chandra, Yi-Ju Chang, Shiyu Chang, Ken Chang, Joseph Chazalon, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Chen, Kun Cheng, Ahana Roy Choudhury, Roger Chylla, Albert Clérigues, Steven Colleman, Ramiro German Rodriguez Colmeiro, Marc Combalia, Anthony Costa, Xiaomeng Cui, Zhenzhen Dai, Lutao Dai, Laura Alexandra Daza, Eric Deutsch, Changxing Ding, Chao Dong, Shidu Dong, Wojciech Dudzik, Zach Eaton-Rosen, Gary Egan, Guilherme Escudero, Théo Estienne, Richard Everson, Jonathan Fabrizio, Yong Fan, Longwei Fang, Xue Feng, Enzo Ferrante, Lucas Fidon, Martin Fischer, Andrew P. French, Naomi Fridman, Huan Fu, David Fuentes, Yaozong Gao, Evan Gates, David Gering, Amir Gholami, Willi Gierke, Ben Glocker, Mingming Gong, Sandra González-Villá, T. Grosges, Yuanfang Guan, Sheng Guo, Sudeep Gupta, Woo-Sup Han, Il Song Han, Konstantin Harmuth, Huiguang He, Aura Hernández-Sabaté, Evelyn Herrmann, Naveen Himthani, Winston Hsu, Cheyu Hsu, Xiaojun Hu, Xiaobin Hu, Yan Hu, Yifan Hu, Rui Hua, Teng-Yi Huang, Weilin Huang, Sabine Van Huffel, Quan Huo, Vivek HV, Khan M. Iftekharuddin, Fabian Isensee, Mobarakol Islam, Aaron S. Jackson, Sachin R. Jambawalikar, Andrew Jesson, Weijian Jian, Peter Jin, V Jeya Maria Jose, Alain Jungo, B Kainz, Konstantinos Kamnitsas, Po-Yu Kao, Ayush Karnawat, Thomas Kellermeier, Adel Kermi, Kurt Keutzer, Mohamed Tarek Khadir, Mahendra Khened, Philipp Kickingereder, Geena Kim, Nik King, Haley Knapp, Urspeter Knecht, Lisa Kohli, Deren Kong, Xiangmao Kong, Simon Koppers, Avinash Kori, Ganapathy Krishnamurthi, Egor Krivov, Piyush Kumar, Kaisar Kushibar, Dmitrii Lachinov, Tryphon Lambrou, Joon Lee, Chengen Lee, Yuehchou Lee, M Lee, Szidonia Lefkovits, Laszlo Lefkovits, James Levitt, Tengfei Li, Hongwei Li, Hongyang Li, Xiaochuan Li, Yuexiang Li, Heng Li, Zhenye Li, Xiaoyu Li, Zeju Li, Xiaogang Li, Wenqi Li, Zheng-Shen Lin, Fengming Lin, Pietro Lio, Chang Liu, Boqiang Liu, Xiang Liu, Mingyuan Liu, Ju Liu, Luyan Liu, Xavier Llado, Marc Moreno Lopez, Pablo Ribalta Lorenzo, Zhentai Lu, Lin Luo, Zhigang Luo, Jun Ma, Kai Ma, Thomas Mackie, Anant Madabushi, Issam Mahmoudi, Klaus H. Maier-Hein, Pradipta Maji, CP Mammen, Andreas Mang, B. S. Manjunath, Michal Marcinkiewicz, S McDonagh, Stephen McKenna, Richard McKinley, Miriam Mehl, Sachin Mehta, Raghav Mehta, Raphael Meier, Christoph Meinel, Dorit Merhof, Craig Meyer, Robert Miller, Sushmita Mitra, Aliasgar Moiyadi, David Molina-Garcia, Miguel A. B. Monteiro, Grzegorz Mrukwa, Andriy Myronenko, Jakub Nalepa, Thuyen Ngo, Dong Nie, Holly Ning, Chen Niu, Nicholas K Nuechterlein, Eric Oermann, Arlindo Oliveira, Diego D. C. Oliveira, Arnau Oliver, Alexander F. I. Osman, Yu-Nian Ou, Sebastien Ourselin, Nikos Paragios, Moo Sung Park, Brad Paschke, J. Gregory Pauloski, Kamlesh Pawar, Nick Pawlowski, Linmin Pei, Suting Peng, Silvio M. Pereira, Julian Perez-Beteta, Victor M. Perez-Garcia, Simon Pezold, Bao Pham, Ashish Phophalia, Gemma Piella, G. N. Pillai, Marie Piraud, Maxim Pisov, Anmol Popli, Michael P. Pound, Reza Pourreza, Prateek Prasanna, Vesna Prkovska, Tony P. Pridmore, Santi Puch, Élodie Puybareau, Buyue Qian, Xu Qiao, Martin Rajchl, Swapnil Rane, Michael Rebsamen, Hongliang Ren, Xuhua Ren, Karthik Revanuru, Mina Rezaei, Oliver Rippel, Luis Carlos Rivera, Charlotte Robert, Bruce Rosen, Daniel Rueckert, Mohammed Safwan, Mostafa Salem, Joaquim Salvi, Irina Sanchez, Irina Sánchez, Heitor M. Santos, Emmett Sartor, Dawid Schellingerhout, Klaudius Scheufele, Matthew R. Scott, Artur A. Scussel, Sara Sedlar, Juan Pablo Serrano-Rubio, N. Jon Shah, Nameetha Shah, Mazhar Shaikh, B. Uma Shankar, Zeina Shboul, Haipeng Shen, Dinggang Shen, Linlin Shen, Haocheng Shen, Varun Shenoy, Feng Shi, Hyung Eun Shin, Hai Shu, Diana Sima, M Sinclair, Orjan Smedby, James M. Snyder, Mohammadreza Soltaninejad, Guidong Song, Mehul Soni, Jean Stawiaski, Shashank Subramanian, Li Sun, Roger Sun, Jiawei Sun, Kay Sun, Yu Sun, Guoxia Sun, Shuang Sun, Yannick R Suter, Laszlo Szilagyi, Sanjay Talbar, DaCheng Tao, Zhongzhao Teng, Siddhesh Thakur, Meenakshi H Thakur, Sameer Tharakan, Pallavi Tiwari, Guillaume Tochon, Tuan Tran, Yuhsiang M. Tsai, Kuan-Lun Tseng, Tran Anh Tuan, Vadim Turlapov, Nicholas Tustison, Maria Vakalopoulou, Sergi Valverde, Rami Vanguri, Evgeny Vasiliev, Jonathan Ventura, Luis Vera, Tom Vercauteren, C. A. Verrastro, Lasitha Vidyaratne, Veronica Vilaplana, Ajeet Vivekanandan, Qian Wang, Chiatse J. Wang, Wei-Chung Wang, Duo Wang, Ruixuan Wang, Yuanyuan Wang, Chunliang Wang, Guotai Wang, Ning Wen, Xin Wen, Leon Weninger, Wolfgang Wick, Shaocheng Wu, Qiang Wu, Yihong Wu, Yong Xia, Yanwu Xu, Xiaowen Xu, Peiyuan Xu, Tsai-Ling Yang, Xiaoping Yang, Hao-Yu Yang, Junlin Yang, Haojin Yang, Guang Yang, Hongdou Yao, Xujiong Ye, Changchang Yin, Brett Young-Moxon, Jinhua Yu, Xiangyu Yue, Songtao Zhang, Angela Zhang, Kun Zhang, Xue-jie Zhang, Lichi Zhang, Xiaoyue Zhang, Yazhuo Zhang, Lei Zhang, Jian-Guo Zhang, Xiang Zhang, Tianhao Zhang, Sicheng Zhao, Yu Zhao, Xiaomei Zhao, Liang Zhao, Yefeng Zheng, Liming Zhong, Chenhong Zhou, Xiaobing Zhou, Fan Zhou, Hongtu Zhu, Jin Zhu, Ying Zhuge, Weiwei Zong, Jayashree Kalpathy-Cramer, Keyvan Farahani, Christos Davatzikos, Koen van Leemput, Bjoern Menze

This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.

Brain Tumor Segmentation Survival Prediction +1

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

Small Organ Segmentation in Whole-body MRI using a Two-stage FCN and Weighting Schemes

no code implementations30 Jul 2018 Vanya V. Valindria, Ioannis Lavdas, Juan Cerrolaza, Eric O. Aboagye, Andrea G. Rockall, Daniel Rueckert, Ben Glocker

Our experiments show that the proposed approach can boost the segmentation accuracy of multiple small organs in whole-body MRI scans.

Deep Generative Models in the Real-World: An Open Challenge from Medical Imaging

no code implementations14 Jun 2018 Xiaoran Chen, Nick Pawlowski, Martin Rajchl, Ben Glocker, Ender Konukoglu

In this paper, we explore the feasibility of using state-of-the-art auto-encoder-based deep generative models, such as variational and adversarial auto-encoders, for one such task: abnormality detection in medical imaging.

Anomaly Detection

NeuroNet: Fast and Robust Reproduction of Multiple Brain Image Segmentation Pipelines

1 code implementation11 Jun 2018 Martin Rajchl, Nick Pawlowski, Daniel Rueckert, Paul M. Matthews, Ben Glocker

NeuroNet is a deep convolutional neural network mimicking multiple popular and state-of-the-art brain segmentation tools including FSL, SPM, and MALPEM.

Brain Image Segmentation Brain Segmentation +1

Semi-Supervised Learning via Compact Latent Space Clustering

no code implementations ICML 2018 Konstantinos Kamnitsas, Daniel C. Castro, Loic Le Folgoc, Ian Walker, Ryutaro Tanno, Daniel Rueckert, Ben Glocker, Antonio Criminisi, Aditya Nori

We present a novel cost function for semi-supervised learning of neural networks that encourages compact clustering of the latent space to facilitate separation.

Nonparametric Density Flows for MRI Intensity Normalisation

1 code implementation7 Jun 2018 Daniel C. Castro, Ben Glocker

With the adoption of powerful machine learning methods in medical image analysis, it is becoming increasingly desirable to aggregate data that is acquired across multiple sites.

Affine Transformation

Graph Saliency Maps through Spectral Convolutional Networks: Application to Sex Classification with Brain Connectivity

no code implementations5 Jun 2018 Salim Arslan, Sofia Ira Ktena, Ben Glocker, Daniel Rueckert

Graph convolutional networks (GCNs) allow to apply traditional convolution operations in non-Euclidean domains, where data are commonly modelled as irregular graphs.

General Classification

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

24 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

Learning-Based Quality Control for Cardiac MR Images

no code implementations25 Mar 2018 Giacomo Tarroni, Ozan Oktay, Wenjia Bai, Andreas Schuh, Hideaki Suzuki, Jonathan Passerat-Palmbach, Antonio de Marvao, Declan P. O'Regan, Stuart Cook, Ben Glocker, Paul M. Matthews, Daniel Rueckert

The results show the capability of the proposed pipeline to correctly detect incomplete or corrupted scans (e. g. on UK Biobank, sensitivity and specificity respectively 88% and 99% for heart coverage estimation, 85% and 95% for motion detection), allowing their exclusion from the analysed dataset or the triggering of a new acquisition.

Motion Detection

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

no code implementations4 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

Implicit Weight Uncertainty in Neural Networks

3 code implementations3 Nov 2017 Nick Pawlowski, Andrew Brock, Matthew C. H. Lee, Martin Rajchl, Ben Glocker

Modern neural networks tend to be overconfident on unseen, noisy or incorrectly labelled data and do not produce meaningful uncertainty measures.

Normalising Flows

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

Efficient variational Bayesian neural network ensembles for outlier detection

1 code implementation20 Mar 2017 Nick Pawlowski, Miguel Jaques, Ben Glocker

In this work we perform outlier detection using ensembles of neural networks obtained by variational approximation of the posterior in a Bayesian neural network setting.

Outlier Detection

Spectral Graph Convolutions for Population-based Disease Prediction

1 code implementation8 Mar 2017 Sarah Parisot, Sofia Ira Ktena, Enzo Ferrante, Matthew Lee, Ricardo Guerrerro Moreno, Ben Glocker, Daniel Rueckert

We demonstrate the potential of the method on the challenging ADNI and ABIDE databases, as a proof of concept of the benefit from integrating contextual information in classification tasks.

Disease Prediction

Distance Metric Learning using Graph Convolutional Networks: Application to Functional Brain Networks

3 code implementations7 Mar 2017 Sofia Ira Ktena, Sarah Parisot, Enzo Ferrante, Martin Rajchl, Matthew Lee, Ben Glocker, Daniel Rueckert

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 or interactions between elements.

Graph Similarity Metric Learning

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

Reverse Classification Accuracy: Predicting Segmentation Performance in the Absence of Ground Truth

no code implementations11 Feb 2017 Vanya V. Valindria, Ioannis Lavdas, Wenjia Bai, Konstantinos Kamnitsas, Eric O. Aboagye, Andrea G. Rockall, Daniel Rueckert, Ben Glocker

In RCA we take the predicted segmentation from a new image to train a reverse classifier which is evaluated on a set of reference images with available ground truth.

General Classification

Reconstructing Subject-Specific Effect Maps

1 code implementation10 Jan 2017 Ender Konukoglu, Ben Glocker

Analyses on the ADNI dataset show that RSM can also improve correlation between subject-specific detections in cortical thickness data and non-imaging markers of Alzheimer's Disease (AD), such as the Mini Mental State Examination Score and Cerebrospinal Fluid amyloid-$\beta$ levels.

Unsupervised domain adaptation in brain lesion segmentation with adversarial networks

no code implementations28 Dec 2016 Konstantinos Kamnitsas, Christian Baumgartner, Christian Ledig, Virginia F. J. Newcombe, Joanna P. Simpson, Andrew D. Kane, David K. Menon, Aditya Nori, Antonio Criminisi, Daniel Rueckert, Ben Glocker

In this work we investigate unsupervised domain adaptation using adversarial neural networks to train a segmentation method which is more invariant to differences in the input data, and which does not require any annotations on the test domain.

Lesion Segmentation Unsupervised Domain Adaptation

Multi-Output Learning for Camera Relocalization

no code implementations CVPR 2014 Abner Guzman-Rivera, Pushmeet Kohli, Ben Glocker, Jamie Shotton, Toby Sharp, Andrew Fitzgibbon, Shahram Izadi

We formulate this problem as inversion of the generative rendering procedure, i. e., we want to find the camera pose corresponding to a rendering of the 3D scene model that is most similar with the observed input.

3D Reconstruction Camera Relocalization

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