Publications from our Researchers

Several of our current PhD candidates and fellow researchers at the Data Science Institute have published, or in the proccess of publishing, papers to present their research.  

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  • Journal article
    Suzuki H, Venkataraman AV, Bai W, Guitton F, Guo Y, Dehghan A, Matthews PMet al., 2019,

    Associations of regional brain structural differences with aging, modifiable risk factors for dementia, and cognitive performance

    , JAMA Network Open, Vol: 2, Pages: 1-19, ISSN: 2574-3805

    Importance Identifying brain regions associated with risk factors for dementia could guide mechanistic understanding of risk factors associated with Alzheimer disease (AD).Objectives To characterize volume changes in brain regions associated with aging and modifiable risk factors for dementia (MRFD) and to test whether volume differences in these regions are associated with cognitive performance.Design, Setting, and Participants This cross-sectional study used data from UK Biobank participants who underwent T1-weighted structural brain imaging from August 5, 2014, to October 14, 2016. A voxelwise linear model was applied to test for regional gray matter volume differences associated with aging and MRFD (ie, hypertension, diabetes, obesity, and frequent alcohol use). The potential clinical relevance of these associations was explored by comparing their neuroanatomical distributions with the regional brain atrophy found with AD. Mediation models for risk factors, brain volume differences, and cognitive measures were tested. The primary hypothesis was that common, overlapping regions would be found. Primary analysis was conducted on April 1, 2018.Main Outcomes and Measures Gray matter regions that showed relative atrophy associated with AD, aging, and greater numbers of MRFD.Results Among 8312 participants (mean [SD] age, 62.4 [7.4] years; 3959 [47.1%] men), aging and 4 major MRFD (ie, hypertension, diabetes, obesity, and frequent alcohol use) had independent negative associations with specific gray matter volumes. These regions overlapped neuroanatomically with those showing lower volumes in participants with AD, including the posterior cingulate cortex, the thalamus, the hippocampus, and the orbitofrontal cortex. Associations between these MRFD and spatial memory were mediated by differences in posterior cingulate cortex volume (β = 0.0014; SE = 0.0006; P = .02).Conclusions and Relevance This cross-sectional study

  • Conference paper
    Halliday BP, Balaban G, Costa CM, Bai W, Porter B, Hatipoglu S, Fereira ND, Izgi C, Corden B, Tayal U, Ware JS, Plank G, Rinaldi CA, Rueckert D, Prasad SK, Bishop Met al., 2019,

    Improving Arrhythmic Risk Stratification in Non-Ischemic Dilated Cardiomyopathy Through the Evaluation of Novel Scar Characteristics Using CMR

    , Scientific Sessions of the American-Heart-Association, Publisher: LIPPINCOTT WILLIAMS & WILKINS, ISSN: 0009-7322
  • Journal article
    Rajpal H, Rosas De Andraca FE, Jensen HJ, 2019,

    Tangled worldview model of opinion dynamics

    , Frontiers in Physics, Vol: 7, ISSN: 2296-424X

    We study the joint evolution of worldviews by proposing a model of opinion dynamics, which is inspired in notions fromevolutionary ecology. Agents update their opinion on a specific issue based on their propensity to change – asserted by thesocial neighbours – weighted by their mutual similarity on other issues. Agents are, therefore, more influenced by neighbourswith similar worldviews (set of opinions on various issues), resulting in a complex co-evolution of each opinion. Simulationsshow that the worldview evolution exhibits events of intermittent polarization when the social network is scale-free. This, in turn,triggers extreme crashes and surges in the popularity of various opinions. Using the proposed model, we highlight the role ofnetwork structure, bounded rationality of agents, and the role of key influential agents in causing polarization and intermittentreformation of worldviews on scale-free networks.

  • Journal article
    Cofré R, Herzog R, Corcoran D, Rosas FEet al., 2019,

    A comparison of the maximum entropy principle across biological spatial scales

    , Entropy: international and interdisciplinary journal of entropy and information studies, Vol: 21, Pages: 1-20, ISSN: 1099-4300

    Despite their differences, biological systems at different spatial scales tend to exhibit common organizational patterns. Unfortunately, these commonalities are often hard to grasp due to the highly specialized nature of modern science and the parcelled terminology employed by various scientific sub-disciplines. To explore these common organizational features, this paper provides a comparative study of diverse applications of the maximum entropy principle, which has found many uses at different biological spatial scales ranging from amino acids up to societies. By presenting these studies under a common approach and language, this paper aims to establish a unified view over these seemingly highly heterogeneous scenarios.

  • Journal article
    Bhuva AN, Bai W, Lau C, Davies RH, Ye Y, Bulluck H, McAlindon E, Culotta V, Swoboda PP, Captur G, Treibel TA, Augusto JB, Knott KD, Seraphim A, Cole GD, Petersen SE, Edwards NC, Greenwood JP, Bucciarelli-Ducci C, Hughes AD, Rueckert D, Moon JC, Manisty CHet al., 2019,

    A multicenter, scan-rescan, human and machine learning CMR study to test generalizability and precision in imaging biomarker analysis

    , Circulation: Cardiovascular Imaging, Vol: 12, Pages: 1-11, ISSN: 1941-9651

    Background:Automated analysis of cardiac structure and function using machine learning (ML) has great potential, but is currently hindered by poor generalizability. Comparison is traditionally against clinicians as a reference, ignoring inherent human inter- and intraobserver error, and ensuring that ML cannot demonstrate superiority. Measuring precision (scan:rescan reproducibility) addresses this. We compared precision of ML and humans using a multicenter, multi-disease, scan:rescan cardiovascular magnetic resonance data set.Methods:One hundred ten patients (5 disease categories, 5 institutions, 2 scanner manufacturers, and 2 field strengths) underwent scan:rescan cardiovascular magnetic resonance (96% within one week). After identification of the most precise human technique, left ventricular chamber volumes, mass, and ejection fraction were measured by an expert, a trained junior clinician, and a fully automated convolutional neural network trained on 599 independent multicenter disease cases. Scan:rescan coefficient of variation and 1000 bootstrapped 95% CIs were calculated and compared using mixed linear effects models.Results:Clinicians can be confident in detecting a 9% change in left ventricular ejection fraction, with greater than half of coefficient of variation attributable to intraobserver variation. Expert, trained junior, and automated scan:rescan precision were similar (for left ventricular ejection fraction, coefficient of variation 6.1 [5.2%–7.1%], P=0.2581; 8.3 [5.6%–10.3%], P=0.3653; 8.8 [6.1%–11.1%], P=0.8620). Automated analysis was 186× faster than humans (0.07 versus 13 minutes).Conclusions:Automated ML analysis is faster with similar precision to the most precise human techniques, even when challenged with real-world scan:rescan data. Assessment of multicenter, multi-vendor, multi-field strength scan:rescan data (available at www.thevolumesresource.com) permits a generalizable assessment of ML precision and may facili

  • Conference paper
    Chen C, Biffi C, Tarroni G, Petersen S, Bai W, Rueckert Det al., 2019,

    Learning shape priors for robust cardiac MR segmentation from multi-view images

    , International Conference on Medical Image Computing and Computer-Assisted Intervention, Publisher: Springer International Publishing, Pages: 523-531, ISSN: 0302-9743

    Cardiac MR image segmentation is essential for the morphological and functional analysis of the heart. Inspired by how experienced clinicians assess the cardiac morphology and function across multiple standard views (i.e. long- and short-axis views), we propose a novel approach which learns anatomical shape priors across different 2D standard views and leverages these priors to segment the left ventricular (LV) myocardium from short-axis MR image stacks. The proposed segmentation method has the advantage of being a 2D network but at the same time incorporates spatial context from multiple, complementary views that span a 3D space. Our method achieves accurate and robust segmentation of the myocardium across different short-axis slices (from apex to base), outperforming baseline models (e.g. 2D U-Net, 3D U-Net) while achieving higher data efficiency. Compared to the 2D U-Net, the proposed method reduces the mean Hausdorff distance (mm) from 3.24 to 2.49 on the apical slices, from 2.34 to 2.09 on the middle slices and from 3.62 to 2.76 on the basal slices on the test set, when only 10% of the training data was used.

  • Journal article
    Balaban G, Halliday BP, Bai W, Porter B, Malvuccio C, Lamata P, Rinaldi CA, Plank G, Rueckert D, Prasad SK, Bishop MJet al., 2019,

    Scar shape analysis and simulated electrical instabilities in a non-ischemic dilated cardiomyopathy patient cohort.

    , PLoS Computational Biology, Vol: 15, Pages: 1-18, ISSN: 1553-734X

    This paper presents a morphological analysis of fibrotic scarring in non-ischemic dilated cardiomyopathy, and its relationship to electrical instabilities which underlie reentrant arrhythmias. Two dimensional electrophysiological simulation models were constructed from a set of 699 late gadolinium enhanced cardiac magnetic resonance images originating from 157 patients. Areas of late gadolinium enhancement (LGE) in each image were assigned one of 10 possible microstructures, which modelled the details of fibrotic scarring an order of magnitude below the MRI scan resolution. A simulated programmed electrical stimulation protocol tested each model for the possibility of generating either a transmural block or a transmural reentry. The outcomes of the simulations were compared against morphological LGE features extracted from the images. Models which blocked or reentered, grouped by microstructure, were significantly different from one another in myocardial-LGE interface length, number of components and entropy, but not in relative area and transmurality. With an unknown microstructure, transmurality alone was the best predictor of block, whereas a combination of interface length, transmurality and number of components was the best predictor of reentry in linear discriminant analysis.

  • Journal article
    Kermani NZ, Pavlidis S, Riley JH, Chung FK, Adcock IM, Guo Y-Ket al., 2019,

    Prediction of longitudinal inflammatory phenotypes using baseline sputum transcriptomics in UBIOPRED

    , EUROPEAN RESPIRATORY JOURNAL, Vol: 54, ISSN: 0903-1936
  • Journal article
    Tiotiu A, Kermani NZ, Agapow P, Saqi M, Guo Y-K, Djukanovic R, Chung KF, Adcock IMet al., 2019,

    Differential macrophage activation in asthmatic sputum using U-BIOPRED transcriptomics

    , EUROPEAN RESPIRATORY JOURNAL, Vol: 54, ISSN: 0903-1936
  • Journal article
    Cofré R, Videla L, Rosas F, 2019,

    An introduction to the non-equilibrium steady states of maximum entropy spike trains

    , Entropy, Vol: 21, Pages: 1-28, ISSN: 1099-4300

    Although most biological processes are characterized by a strong temporal asymmetry, several popular mathematical models neglect this issue. Maximum entropy methods provide a principled way of addressing time irreversibility, which leverages powerful results and ideas from the literature of non-equilibrium statistical mechanics. This tutorial provides a comprehensive overview of these issues, with a focus in the case of spike train statistics. We provide a detailed account of the mathematical foundations and work out examples to illustrate the key concepts and results from non-equilibrium statistical mechanics.

  • Conference paper
    Duan J, Schlemper J, Qin C, Ouyang C, Bai W, Biffi C, Bello G, Statton B, O’Regan DP, Rueckert Det al., 2019,

    VS-Net: variable splitting network for accelerated parallel MRI reconstruction

    , International Conference on Medical Image Computing and Computer-Assisted Intervention, Publisher: Springer International Publishing, Pages: 713-722, ISSN: 0302-9743

    In this work, we propose a deep learning approach for parallel magnetic resonance imaging (MRI) reconstruction, termed a variable splitting network (VS-Net), for an efficient, high-quality reconstruction of undersampled multi-coil MR data. We formulate the generalized parallel compressed sensing reconstruction as an energy minimization problem, for which a variable splitting optimization method is derived. Based on this formulation we propose a novel, end-to-end trainable deep neural network architecture by unrolling the resulting iterative process of such variable splitting scheme. VS-Net is evaluated on complex valued multi-coil knee images for 4-fold and 6-fold acceleration factors. We show that VS-Net outperforms state-of-the-art deep learning reconstruction algorithms, in terms of reconstruction accuracy and perceptual quality. Our code is publicly available at https://github.com/j-duan/VS-Net.

  • Conference paper
    Wang S, Dai C, Mo Y, Angelini E, Guo Y, Bai Wet al., 2019,

    Automatic Brain Tumour Segmentation and Biophysics-Guided Survival Prediction

    , MICCAI BraTS 2019 Challenge

    Gliomas are the most common malignant brain tumourswith intrinsicheterogeneity. Accurate segmentation of gliomas and theirsub-regions onmulti-parametric magnetic resonance images (mpMRI)is of great clinicalimportance, which defines tumour size, shape andappearance and providesabundant information for preoperative diag-nosis, treatment planning andsurvival prediction. Recent developmentson deep learning have significantlyimproved the performance of auto-mated medical image segmentation. In thispaper, we compare severalstate-of-the-art convolutional neural network modelsfor brain tumourimage segmentation. Based on the ensembled segmentation, wepresenta biophysics-guided prognostic model for patient overall survivalpredic-tion which outperforms a data-driven radiomics approach. Our methodwonthe second place of the MICCAI 2019 BraTS Challenge for theoverall survivalprediction.

  • Journal article
    Duan J, Bello G, Schlemper J, Bai W, Dawes TJW, Biffi C, Marvao AD, Doumou G, O'Regan DP, Rueckert Det al., 2019,

    Automatic 3D bi-ventricular segmentation of cardiac images by a shape-refined multi-task deep learning approach

    , IEEE Transactions on Medical Imaging, Vol: 38, Pages: 2151-2164, ISSN: 0278-0062

    Deep learning approaches have achieved state-of-the-art performance incardiac magnetic resonance (CMR) image segmentation. However, most approaches have focused on learning image intensity features for segmentation, whereas the incorporation of anatomical shape priors has received less attention. In this paper, we combine a multi-task deep learning approach with atlas propagation to develop a shape-constrained bi-ventricular segmentation pipeline for short-axis CMR volumetric images. The pipeline first employs a fully convolutional network (FCN) that learns segmentation and landmark localisation tasks simultaneously. The architecture of the proposed FCN uses a 2.5D representation, thus combining the computational advantage of 2D FCNs networks and the capability of addressing 3D spatial consistency without compromising segmentation accuracy. Moreover, the refinement step is designed to explicitly enforce a shape constraint and improve segmentation quality. This step is effective for overcoming image artefacts (e.g. due to different breath-hold positions and large slice thickness), which preclude the creation of anatomically meaningful 3D cardiac shapes. The proposed pipeline is fully automated, due to network's ability to infer landmarks, which are then used downstream in the pipeline to initialise atlas propagation. We validate the pipeline on 1831 healthy subjects and 649 subjects with pulmonary hypertension. Extensive numerical experiments on the two datasets demonstrate that our proposed method is robust and capable of producing accurate, high-resolution and anatomically smooth bi-ventricular3D models, despite the artefacts in input CMR volumes.

  • Conference paper
    Dai C, Mo Y, Angelini E, Guo Y, Bai Wet al., 2019,

    Transfer learning from partial annotations for whole brain segmentation

    , International Workshop on Medical Image Learning with Less Labels and Imperfect Data

    Brain MR image segmentation is a key task in neuroimaging studies. It is commonly conducted using standard computational tools, such as FSL, SPM, multi-atlas segmentation etc, which are often registration-based and suffer from expensive computation cost. Recently, there is an increased interest using deep neural networks for brain image segmentation, which have demonstrated advantages in both speed and performance. However, neural networks-based approaches normally require a large amount of manual annotations for optimising the massive amount of network parameters. For 3D networks used in volumetric image segmentation, this has become a particular challenge, as a 3D network consists of many more parameters compared to its 2D counterpart. Manual annotation of 3D brain images is extremely time-consuming and requires extensive involvement of trained experts. To address the challenge with limited manual annotations, here we propose a novel multi-task learning framework for brain image segmentation, which utilises a large amount of automatically generated partial annotations together with a small set of manually created full annotations for network training. Our method yields a high performance comparable to state-of-the-art methods for whole brain segmentation.

  • Conference paper
    Gadotti A, Houssiau F, Rocher L, Livshits B, de Montjoye Y-Aet al., 2019,

    When the signal is in the noise: exploiting Diffix's sticky noise

    , 28th USENIX Security Symposium (USENIX Security '19), Publisher: USENIX, Pages: 1081-1098

    Anonymized data is highly valuable to both businesses and researchers. A large body of research has however shown the strong limits of the de-identification release-and-forget model, where data is anonymized and shared. This has led to the development of privacy-preserving query-based systems. Based on the idea of “sticky noise”, Diffix has been recently pro-posed as a novel query-based mechanism satisfying alone the EU Article 29 Working Party’s definition of anonymization. According to its authors, Diffix adds less noise to answers than solutions based on differential privacy while allowing for an unlimited number of queries.This paper presents a new class of noise-exploitation attacks, exploiting the noise added by the system to infer privateinformation about individuals in the dataset. Our first differential attack uses samples extracted from Diffix in a likelihood ratio test to discriminate between two probability distributions.We show that using this attack against a synthetic best-case dataset allows us to infer private information with 89.4% accuracy using only 5 attributes. Our second cloning attack uses dummy conditions that conditionally strongly affect the output of the query depending on the value of the private attribute. Using this attack on four real-world datasets, we show that we can infer private attributes of at least 93% of the users in the dataset with accuracy between 93.3% and 97.1%, issuing a median of 304 queries per user. We show how to optimize this attack, targeting 55.4% of the users and achieving 91.7% accuracy, using a maximum of only 32 queries per user. Our attacks demonstrate that adding data-dependent noise, as done by Diffix, is not sufficient to prevent inference of private attributes. We furthermore argue that Diffix alone fails to satisfy Art. 29 WP’s definition of anonymization. We conclude by discussing how non-provable privacy-preserving systems can be combined with fundamental security principles su

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