no code implementations • 1 May 2017 • Ryutaro Tanno, Daniel E. Worrall, Aurobrata Ghosh, Enrico Kaden, Stamatios N. Sotiropoulos, Antonio Criminisi, Daniel C. Alexander
In this work, we investigate the value of uncertainty modeling in 3D super-resolution with convolutional neural networks (CNNs).
no code implementations • 18 Jun 2018 • Felix J. S. Bragman, Ryutaro Tanno, Zach Eaton-Rosen, Wenqi Li, David J. Hawkes, Sebastien Ourselin, Daniel C. Alexander, Jamie R. McClelland, M. Jorge Cardoso
Multi-task neural network architectures provide a mechanism that jointly integrates information from distinct sources.
1 code implementation • ICLR 2019 • Ryutaro Tanno, Kai Arulkumaran, Daniel C. Alexander, Antonio Criminisi, Aditya Nori
Deep neural networks and decision trees operate on largely separate paradigms; typically, the former performs representation learning with pre-specified architectures, while the latter is characterised by learning hierarchies over pre-specified features with data-driven architectures.
1 code implementation • 16 Aug 2018 • Stefano B. Blumberg, Ryutaro Tanno, Iasonas Kokkinos, Daniel C. Alexander
In this paper we address the memory demands that come with the processing of 3-dimensional, high-resolution, multi-channeled medical images in deep learning.
1 code implementation • 11 Jan 2019 • Razvan V. Marinescu, Arman Eshaghi, Marco Lorenzi, Alexandra L. Young, Neil P. Oxtoby, Sara Garbarino, Sebastian J. Crutch, Daniel C. Alexander
Here we present DIVE: Data-driven Inference of Vertexwise Evolution.
2 code implementations • 11 Jan 2019 • Razvan V. Marinescu, Marco Lorenzi, Stefano B. Blumberg, Alexandra L. Young, Pere P. Morell, Neil P. Oxtoby, Arman Eshaghi, Keir X. Yong, Sebastian J. Crutch, Polina Golland, Daniel C. Alexander
DKT infers robust multimodal biomarker trajectories in rare neurodegenerative diseases even when only limited, unimodal data is available, by transferring information from larger multimodal datasets from common neurodegenerative diseases.
1 code implementation • CVPR 2019 • Ryutaro Tanno, Ardavan Saeedi, Swami Sankaranarayanan, Daniel C. Alexander, Nathan Silberman
We provide a theoretical argument as to how the regularization is essential to our approach both for the case of single annotator and multiple annotators.
2 code implementations • 21 May 2019 • Razvan V. Marinescu, Arman Eshaghi, Daniel C. Alexander, Polina Golland
Compared to existing visualisation software (i. e. Freesurfer, SPM, 3D Slicer), BrainPainter has three key advantages: (1) it does not require the input data to be in a specialised format, allowing BrainPainter to be used in combination with any neuroimaging analysis tools, (2) it can visualise both cortical and subcortical structures and (3) it can be used to generate movies showing dynamic processes, e. g. propagation of pathology on the brain.
Graphics Image and Video Processing
no code implementations • 5 Jul 2019 • Daniele Ravi, Daniel C. Alexander, Neil P. Oxtoby
Simulating images representative of neurodegenerative diseases is important for predicting patient outcomes and for validation of computational models of disease progression.
no code implementations • 26 Jul 2019 • Stefano B. Blumberg, Marco Palombo, Can Son Khoo, Chantal M. W. Tax, Ryutaro Tanno, Daniel C. Alexander
Specifically, we introduce the Multi Stage Prediction (MSP) Network, a MTL framework that incorporates neural networks of potentially disparate architectures, trained for different individual acquisition platforms, into a larger architecture that is refined in unison.
no code implementations • 31 Jul 2019 • Ryutaro Tanno, Daniel Worrall, Enrico Kaden, Aurobrata Ghosh, Francesco Grussu, Alberto Bizzi, Stamatios N. Sotiropoulos, Antonio Criminisi, Daniel C. Alexander
Here we introduce methods to characterise different components of uncertainty in such problems and demonstrate the ideas using diffusion MRI super-resolution.
no code implementations • ICCV 2019 • Felix J. S. Bragman, Ryutaro Tanno, Sebastien Ourselin, Daniel C. Alexander, M. Jorge Cardoso
The performance of multi-task learning in Convolutional Neural Networks (CNNs) hinges on the design of feature sharing between tasks within the architecture.
no code implementations • 15 Sep 2019 • Hongxiang Lin, Matteo Figini, Ryutaro Tanno, Stefano B. Blumberg, Enrico Kaden, Godwin Ogbole, Biobele J. Brown, Felice D'Arco, David W. Carmichael, Ikeoluwa Lagunju, Helen J. Cross, Delmiro Fernandez-Reyes, Daniel C. Alexander
In this paper we propose a probabilistic decimation simulator to improve robustness of model training.
no code implementations • 3 Dec 2019 • Daniele Ravi, Stefano B. Blumberg, Silvia Ingala, Frederik Barkhof, Daniel C. Alexander, Neil P. Oxtoby
To evaluate our approach, we trained the framework on 9852 T1-weighted MRI scans from 876 participants in the Alzheimer's Disease Neuroimaging Initiative dataset and held out a separate test set of 1283 MRI scans from 170 participants for quantitative and qualitative assessment of the personalised time series of synthetic images.
4 code implementations • 9 Feb 2020 • Razvan V. Marinescu, Neil P. Oxtoby, Alexandra L. Young, Esther E. Bron, Arthur W. Toga, Michael W. Weiner, Frederik Barkhof, Nick C. Fox, Arman Eshaghi, Tina Toni, Marcin Salaterski, Veronika Lunina, Manon Ansart, Stanley Durrleman, Pascal Lu, Samuel Iddi, Dan Li, Wesley K. Thompson, Michael C. Donohue, Aviv Nahon, Yarden Levy, Dan Halbersberg, Mariya Cohen, Huiling Liao, Tengfei Li, Kaixian Yu, Hongtu Zhu, Jose G. Tamez-Pena, Aya Ismail, Timothy Wood, Hector Corrada Bravo, Minh Nguyen, Nanbo Sun, Jiashi Feng, B. T. Thomas Yeo, Gang Chen, Ke Qi, Shiyang Chen, Deqiang Qiu, Ionut Buciuman, Alex Kelner, Raluca Pop, Denisa Rimocea, Mostafa M. Ghazi, Mads Nielsen, Sebastien Ourselin, Lauge Sorensen, Vikram Venkatraghavan, Keli Liu, Christina Rabe, Paul Manser, Steven M. Hill, James Howlett, Zhiyue Huang, Steven Kiddle, Sach Mukherjee, Anais Rouanet, Bernd Taschler, Brian D. M. Tom, Simon R. White, Noel Faux, Suman Sedai, Javier de Velasco Oriol, Edgar E. V. Clemente, Karol Estrada, Leon Aksman, Andre Altmann, Cynthia M. Stonnington, Yalin Wang, Jianfeng Wu, Vivek Devadas, Clementine Fourrier, Lars Lau Raket, Aristeidis Sotiras, Guray Erus, Jimit Doshi, Christos Davatzikos, Jacob Vogel, Andrew Doyle, Angela Tam, Alex Diaz-Papkovich, Emmanuel Jammeh, Igor Koval, Paul Moore, Terry J. Lyons, John Gallacher, Jussi Tohka, Robert Ciszek, Bruno Jedynak, Kruti Pandya, Murat Bilgel, William Engels, Joseph Cole, Polina Golland, Stefan Klein, Daniel C. Alexander
TADPOLE's unique results suggest that current prediction algorithms provide sufficient accuracy to exploit biomarkers related to clinical diagnosis and ventricle volume, for cohort refinement in clinical trials for Alzheimer's disease.
no code implementations • 16 Mar 2020 • Matteo Figini, Hongxiang Lin, Godwin Ogbole, Felice D Arco, Stefano B. Blumberg, David W. Carmichael, Ryutaro Tanno, Enrico Kaden, Biobele J. Brown, Ikeoluwa Lagunju, Helen J. Cross, Delmiro Fernandez-Reyes, Daniel C. Alexander
1. 5T or 3T scanners are the current standard for clinical MRI, but low-field (<1T) scanners are still common in many lower- and middle-income countries for reasons of cost and robustness to power failures.
1 code implementation • 29 Jul 2020 • Chen Jin, Ryutaro Tanno, Mou-Cheng Xu, Thomy Mertzanidou, Daniel C. Alexander
We demonstrate on three publicly available high-resolution image datasets that the foveation module consistently improves segmentation performance over the cases trained with patches of fixed FoV/resolution trade-off.
1 code implementation • 29 Jul 2020 • Mou-Cheng Xu, Neil P. Oxtoby, Daniel C. Alexander, Joseph Jacob
We compared our methods with state-of-the-art attention mechanisms in medical imaging, including self-attention, spatial-attention and spatial-channel mixed attention.
3 code implementations • 31 Jul 2020 • Le Zhang, Ryutaro Tanno, Mou-Cheng Xu, Chen Jin, Joseph Jacob, Olga Ciccarelli, Frederik Barkhof, Daniel C. Alexander
Recent years have seen increasing use of supervised learning methods for segmentation tasks.
no code implementations • 2 Nov 2020 • Peter A. Wijeratne, Daniel C. Alexander
We use clinical, imaging and biofluid data from the Alzheimer's Disease Neuroimaging Initiative to demonstrate the validity and utility of our model.
1 code implementation • 4 Nov 2020 • Yunguan Fu, Nina Montaña Brown, Shaheer U. Saeed, Adrià Casamitjana, Zachary M. C. Baum, Rémi Delaunay, Qianye Yang, Alexander Grimwood, Zhe Min, Stefano B. Blumberg, Juan Eugenio Iglesias, Dean C. Barratt, Ester Bonmati, Daniel C. Alexander, Matthew J. Clarkson, Tom Vercauteren, Yipeng Hu
DeepReg (https://github. com/DeepRegNet/DeepReg) is a community-supported open-source toolkit for research and education in medical image registration using deep learning.
1 code implementation • 24 Dec 2020 • Juan Eugenio Iglesias, Benjamin Billot, Yael Balbastre, Azadeh Tabari, John Conklin, Daniel C. Alexander, Polina Golland, Brian L. Edlow, Bruce Fischl
Most existing algorithms for automatic 3D morphometry of human brain MRI scans are designed for data with near-isotropic voxels at approximately 1 mm resolution, and frequently have contrast constraints as well - typically requiring T1 scans (e. g., MP-RAGE).
1 code implementation • 2 Apr 2021 • Loic Peter, Daniel C. Alexander, Caroline Magnain, Juan Eugenio Iglesias
In this paper, we introduce a principled strategy for the construction of a gold standard in deformable registration.
2 code implementations • 25 Apr 2021 • Yukun Zhou, MouCheng Xu, Yipeng Hu, Hongxiang Lin, Joseph Jacob, Pearse A. Keane, Daniel C. Alexander
Accurate multi-class segmentation is a long-standing challenge in medical imaging, especially in scenarios where classes share strong similarity.
1 code implementation • ICLR 2022 • Chen Jin, Ryutaro Tanno, Thomy Mertzanidou, Eleftheria Panagiotaki, Daniel C. Alexander
Many computer vision systems require low-cost segmentation algorithms based on deep learning, either because of the enormous size of input images or limited computational budget.
2 code implementations • 23 Oct 2021 • Mou-Cheng Xu, Yukun Zhou, Chen Jin, Marius de Groot, Neil P. Oxtoby, Daniel C. Alexander, Joseph Jacob
The state-of-the-art SSL methods in image classification utilise consistency regularisation to learn unlabelled predictions which are invariant to input level perturbations.
1 code implementation • 19 Nov 2021 • Ashkan Pakzad, Wing Keung Cheung, Kin Quan, Nesrin Mogulkoc, Coline H. M. Van Moorsel, Brian J. Bartholmai, Hendrik W. Van Es, Alper Ezircan, Frouke Van Beek, Marcel Veltkamp, Ronald Karwoski, Tobias Peikert, Ryan D. Clay, Finbar Foley, Cassandra Braun, Recep Savas, Carole Sudre, Tom Doel, Daniel C. Alexander, Peter Wijeratne, David Hawkes, Yipeng Hu, John R Hurst, Joseph Jacob
AirQuant is an open-source pipeline that avoids limitations of existing airway quantification algorithms and has clinical interpretability.
2 code implementations • British Machine Vision Conference (BMVC) 2021 • Seunghoi Kim, Daniel C. Alexander
To overcome these problems, we propose a) a graph convolutional network (GCN) in an adversarial learning scheme where a discriminator network provides a segmentation network with informative information to improve segmentation accuracy and b) a graph convolution, GeoEdgeConv, as a means of local feature aggregation to improve segmentation accuracy and space and time complexities.
Ranked #6 on 3D Part Segmentation on ShapeNet-Part
no code implementations • 15 Dec 2021 • Esther E. Bron, Stefan Klein, Annika Reinke, Janne M. Papma, Lena Maier-Hein, Daniel C. Alexander, Neil P. Oxtoby
Key for increasing impact in this way are larger testing data sizes, which could be reached by sharing algorithms rather than data to exploit data that cannot be shared.
no code implementations • 12 Mar 2022 • Yukun Zhou, MouCheng Xu, Yipeng Hu, Stefano B. Blumberg, An Zhao, Siegfried K. Wagner, Pearse A. Keane, Daniel C. Alexander
Estimating clinically-relevant vascular features following vessel segmentation is a standard pipeline for retinal vessel analysis, which provides potential ocular biomarkers for both ophthalmic disease and systemic disease.
1 code implementation • 17 Mar 2022 • Stefano B. Blumberg, Hongxiang Lin, Francesco Grussu, Yukun Zhou, Matteo Figini, Daniel C. Alexander
We build upon a recent dual-network approach that won the MICCAI MUlti-DIffusion (MUDI) quantitative MRI measurement sampling-reconstruction challenge, but suffers from deep learning training instability, by subsampling with a hard decision boundary.
1 code implementation • 19 Mar 2022 • Mou-Cheng Xu, Yu-Kun Zhou, Chen Jin, Stefano B Blumberg, Frederick J Wilson, Marius deGroot, Daniel C. Alexander, Neil P. Oxtoby, Joseph Jacob
We propose MisMatch, a novel consistency-driven semi-supervised segmentation framework which produces predictions that are invariant to learnt feature perturbations.
2 code implementations • 21 Mar 2022 • Ahmed H. Shahin, Joseph Jacob, Daniel C. Alexander, David Barber
To this end, we propose a probabilistic model that captures the dependencies between the observed clinical variables and imputes missing ones.
no code implementations • 7 Jun 2022 • Daniele Ravi, Frederik Barkhof, Daniel C. Alexander, Lemuel Puglisi, Geoffrey JM Parker, Arman Eshaghi
To tackle this problem, we propose a framework with four main components: 1) artefact generators inspired by magnetic resonance physics to corrupt brain MRI scans and augment a training dataset, 2) abstract and engineered features to represent images compactly, 3) a feature selection process depending on the artefact class to improve classification, and 4) SVM classifiers to identify artefacts.
1 code implementation • 8 Aug 2022 • Mou-Cheng Xu, Yukun Zhou, Chen Jin, Marius de Groot, Daniel C. Alexander, Neil P. Oxtoby, Yipeng Hu, Joseph Jacob
Secondly, we propose a semi-supervised medical image segmentation method purely based on the original pseudo labelling, namely SegPL.
1 code implementation • 5 Oct 2022 • Jason P. Lim, Stefano B. Blumberg, Neil Narayan, Sean C. Epstein, Daniel C. Alexander, Marco Palombo, Paddy J. Slator
In this paper, we demonstrate self-supervised machine learning model fitting for a directional microstructural model.
1 code implementation • 13 Oct 2022 • Stefano B. Blumberg, Paddy J. Slator, Daniel C. Alexander
Here we present a new paradigm for experimental design that simultaneously optimizes the design (set of image channels) and trains a machine-learning model to execute a user-specified image-analysis task.
no code implementations • 17 Oct 2022 • Stefano B. Blumberg, Daniele Raví, Mou-Cheng Xu, Matteo Figini, Iasonas Kokkinos, Daniel C. Alexander
Transposed convolution is crucial for generating high-resolution outputs, yet has received little attention compared to convolution layers.
no code implementations • 25 Feb 2023 • Lemuel Puglisi, Frederik Barkhof, Daniel C. Alexander, Geoffrey JM Parker, Arman Eshaghi, Daniele Ravì
Our framework is a semi-self-supervised contrastive deep learning approach with three main innovations.
no code implementations • 19 Mar 2023 • Yaozhi Lu, Shahab Aslani, An Zhao, Ahmed Shahin, David Barber, Mark Emberton, Daniel C. Alexander, Joseph Jacob
The Cox neural network can achieve an IPCW C-index of 0. 75 on the internal dataset and 0. 69 on an external dataset.
1 code implementation • 26 Apr 2023 • Hongxiang Lin, Matteo Figini, Felice D'Arco, Godwin Ogbole, Ryutaro Tanno, Stefano B. Blumberg, Lisa Ronan, Biobele J. Brown, David W. Carmichael, Ikeoluwa Lagunju, Judith Helen Cross, Delmiro Fernandez-Reyes, Daniel C. Alexander
Here, we present Image Quality Transfer (IQT) to enhance low-field structural MRI by estimating from a low-field image the image we would have obtained from the same subject at high field.
1 code implementation • 2 May 2023 • MouCheng Xu, Yukun Zhou, Chen Jin, Marius de Groot, Daniel C. Alexander, Neil P. Oxtoby, Yipeng Hu, Joseph Jacob
In the remainder of the paper, we showcase the applications of pseudo-labelling and its generalised form, Bayesian Pseudo-Labelling, in the semi-supervised segmentation of medical images.
no code implementations • 5 May 2023 • Henry F. J. Tregidgo, Sonja Soskic, Mark D. Olchanyi, Juri Althonayan, Benjamin Billot, Chiara Maffei, Polina Golland, Anastasia Yendiki, Daniel C. Alexander, Martina Bocchetta, Jonathan D. Rohrer, Juan Eugenio Iglesias
Some tools have attempted to incorporate information from diffusion MRI in the segmentation to refine these boundaries, but do not generalise well across diffusion MRI acquisitions.
no code implementations • 13 Jul 2023 • Christopher S. Parker, Anna Schroder, Sean C. Epstein, James Cole, Daniel C. Alexander, HUI ZHANG
Results: Networks trained with NLR loss show higher estimation accuracy than MSE for the ADC and IVIM diffusion coefficients as SNR decreases, with minimal loss of precision or total error.
no code implementations • 29 Jul 2023 • Wing Keung Cheung, Jeremy Kalindjian, Robert Bell, Arjun Nair, Leon J. Menezes, Riyaz Patel, Simon Wan, Kacy Chou, Jiahang Chen, Ryo Torii, Rhodri H. Davies, James C. Moon, Daniel C. Alexander, Joseph Jacob
Early detection and diagnosis of coronary artery disease (CAD) could save lives and reduce healthcare costs.
no code implementations • 29 Jul 2023 • Wing Keung Cheung, Ashkan Pakzad, Nesrin Mogulkoc, Sarah Needleman, Bojidar Rangelov, Eyjolfur Gudmundsson, An Zhao, Mariam Abbas, Davina McLaverty, Dimitrios Asimakopoulos, Robert Chapman, Recep Savas, Sam M Janes, Yipeng Hu, Daniel C. Alexander, John R Hurst, Joseph Jacob
In this regard, airway segmentation plays a critical role in the production of the outline of the entire airway tree to enable estimation of disease extent and severity.
no code implementations • 10 Aug 2023 • Tiantian He, Elinor Thompson, Anna Schroder, Neil P. Oxtoby, Ahmed Abdulaal, Frederik Barkhof, Daniel C. Alexander
We account for the heterogeneity of disease by fitting the model at the individual level, allowing the epicenters and rate of progression to vary among subjects.
2 code implementations • 7 Sep 2023 • Ahmed H. Shahin, An Zhao, Alexander C. Whitehead, Daniel C. Alexander, Joseph Jacob, David Barber
We demonstrate that our approach forms a consistent estimator for the event model parameters, even in the absence of uncensored data.
1 code implementation • 11 Nov 2023 • Seunghoi Kim, Henry F. J. Tregidgo, Ahmed K. Eldaly, Matteo Figini, Daniel C. Alexander
Low-field (LF) MRI scanners (<1T) are still prevalent in settings with limited resources or unreliable power supply.
1 code implementation • 28 Nov 2023 • Peirong Liu, Oula Puonti, Xiaoling Hu, Daniel C. Alexander, Juan E. Iglesias
We present new metrics to validate the intra- and inter-subject robustness of Brain-ID features, and evaluate their performance on four downstream applications, covering contrast-independent (anatomy reconstruction/contrast synthesis, brain segmentation), and contrast-dependent (super-resolution, bias field estimation) tasks.
no code implementations • 9 Apr 2024 • Seunghoi Kim, Chen Jin, Tom Diethe, Matteo Figini, Henry F. J. Tregidgo, Asher Mullokandov, Philip Teare, Daniel C. Alexander
We hypothesize such hallucinations result from local OOD regions in the conditional images.