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
    Bai W, Suzuki H, Huang J, Francis C, Wang S, Tarroni G, Guitton F, Aung N, Fung K, Petersen SE, Piechnik SK, Neubauer S, Evangelou E, Dehghan A, O'Regan DP, Wilkins MR, Guo Y, Matthews PM, Rueckert Det al., 2020,

    A population-based phenome-wide association study of cardiac and aortic structure and function

    , Nature Medicine, Vol: 26, Pages: 1654-1662, ISSN: 1078-8956

    Differences in cardiac and aortic structure and function are associated with cardiovascular diseases and a wide range of other types of disease. Here we analyzed cardiovascular magnetic resonance images from a population-based study, the UK Biobank, using an automated machine-learning-based analysis pipeline. We report a comprehensive range of structural and functional phenotypes for the heart and aorta across 26,893 participants, and explore how these phenotypes vary according to sex, age and major cardiovascular risk factors. We extended this analysis with a phenome-wide association study, in which we tested for correlations of a wide range of non-imaging phenotypes of the participants with imaging phenotypes. We further explored the associations of imaging phenotypes with early-life factors, mental health and cognitive function using both observational analysis and Mendelian randomization. Our study illustrates how population-based cardiac and aortic imaging phenotypes can be used to better define cardiovascular disease risks as well as heart–brain health interactions, highlighting new opportunities for studying disease mechanisms and developing image-based biomarkers.

  • Journal article
    Moriconi R, Deisenroth M, Karri S, 2020,

    High-dimensional Bayesian optimization usinglow-dimensional feature spaces

    , Machine Learning, Vol: 109, Pages: 1925-1943, ISSN: 0885-6125

    Bayesian optimization (BO) is a powerful approach for seeking the global optimum of expensive black-box functions and has proven successful for fine tuning hyper-parameters of machine learning models. However, BO is practically limited to optimizing 10–20 parameters. To scale BO to high dimensions, we usually make structural assumptions on the decomposition of the objective and/or exploit the intrinsic lower dimensionality of the problem, e.g. by using linear projections. We could achieve a higher compression rate with nonlinear projections, but learning these nonlinear embeddings typically requires much data. This contradicts the BO objective of a relatively small evaluation budget. To address this challenge, we propose to learn a low-dimensional feature space jointly with (a) the response surface and (b) a reconstruction mapping. Our approach allows for optimization of BO’s acquisition function in the lower-dimensional subspace, which significantly simplifies the optimization problem. We reconstruct the original parameter space from the lower-dimensional subspace for evaluating the black-box function. For meaningful exploration, we solve a constrained optimization problem.

  • Conference paper
    Rosas De Andraca FE, Mediano P, Biehl M, Chandaria S, Polani Det al., 2020,

    Causal Blankets: Theory and Algorithmic Framework

    , ECML/PKDD 2020
  • Journal article
    Cofré R, Herzog R, Mediano PAM, Piccinini J, Rosas FE, Sanz Perl Y, Tagliazucchi Eet al., 2020,

    Whole-brain models to explore altered states of consciousness from the bottom up

    , Brain Sciences, Vol: 10, ISSN: 2076-3425

    The scope of human consciousness includes states departing from what most of us experience as ordinary wakefulness. These altered states of consciousness constitute a prime opportunity to study how global changes in brain activity relate to different varieties of subjective experience. We consider the problem of explaining how global signatures of altered consciousness arise from the interplay between large-scale connectivity and local dynamical rules that can be traced to known properties of neural tissue. For this purpose, we advocate a research program aimed at bridging the gap between bottom-up generative models of whole-brain activity and the top-down signatures proposed by theories of consciousness. Throughout this paper, we define altered states of consciousness, discuss relevant signatures of consciousness observed in brain activity, and introduce whole-brain models to explore the biophysics of altered consciousness from the bottom-up. We discuss the potential of our proposal in view of the current state of the art, give specific examples of how this research agenda might play out, and emphasize how a systematic investigation of altered states of consciousness via bottom-up modeling may help us better understand the biophysical, informational, and dynamical underpinnings of consciousness.

  • Journal article
    Fernando S, AmadorDíazLópez J, Şerban O, Gómez-Romero J, Molina-Solana M, Guo Yet al., 2020,

    Towards a large-scale twitter observatory for political events

    , Future Generation Computer Systems, Vol: 110, Pages: 976-983, ISSN: 0167-739X

    Explosion in usage of social media has made its analysis a relevant topic of interest, and particularly so in the political science area. Within Data Science, no other techniques are more widely accepted and appealing than visualisation. However, with datasets growing in size, visualisation tools also require a paradigm shift to remain useful in big data contexts. This work presents our proposal for a Large-Scale Twitter Observatory that enables researchers to efficiently retrieve, analyse and visualise data from this social network to gain actionable insights and knowledge related with political events. In addition to describing the supporting technologies, we put forward a working pipeline and validate the setup with different examples.

  • Journal article
    Mo Y, Wang S, Dai C, Zhou R, Teng Z, Bai W, Guo Yet al., 2020,

    Efficient Deep Representation Learning by Adaptive Latent Space Sampling

    Supervised deep learning requires a large amount of training samples withannotations (e.g. label class for classification task, pixel- or voxel-wisedlabel map for segmentation tasks), which are expensive and time-consuming toobtain. During the training of a deep neural network, the annotated samples arefed into the network in a mini-batch way, where they are often regarded ofequal importance. However, some of the samples may become less informativeduring training, as the magnitude of the gradient start to vanish for thesesamples. In the meantime, other samples of higher utility or hardness may bemore demanded for the training process to proceed and require moreexploitation. To address the challenges of expensive annotations and loss ofsample informativeness, here we propose a novel training framework whichadaptively selects informative samples that are fed to the training process.The adaptive selection or sampling is performed based on a hardness-awarestrategy in the latent space constructed by a generative model. To evaluate theproposed training framework, we perform experiments on three differentdatasets, including MNIST and CIFAR-10 for image classification task and amedical image dataset IVUS for biophysical simulation task. On all threedatasets, the proposed framework outperforms a random sampling method, whichdemonstrates the effectiveness of proposed framework.

  • Journal article
    Meyer H, Dawes T, Serrani M, Bai W, Tokarczuk P, Cai J, Simoes Monteiro de Marvao A, Henry A, Lumbers T, Gierten J, Thumberger T, Wittbrodt J, Ware J, Rueckert D, Matthews P, Prasad S, Costantino M, Cook S, Birney E, O'Regan Det al., 2020,

    Genetic and functional insights into the fractal structure of the heart

    , Nature, Vol: 584, Pages: 589-594, ISSN: 0028-0836

    The inner surfaces of the human heart are covered by a complex network of muscular strands that is thought to be a vestigeof embryonic development.1,2 The function of these trabeculae in adults and their genetic architecture are unknown. Toinvestigate this we performed a genome-wide association study using fractal analysis of trabecular morphology as animage-derived phenotype in 18,096 UK Biobank participants. We identified 16 significant loci containing genes associatedwith haemodynamic phenotypes and regulation of cytoskeletal arborisation.3,4 Using biomechanical simulations and humanobservational data, we demonstrate that trabecular morphology is an important determinant of cardiac performance. Throughgenetic association studies with cardiac disease phenotypes and Mendelian randomisation, we find a causal relationshipbetween trabecular morphology and cardiovascular disease risk. These findings suggest an unexpected role for myocardialtrabeculae in the function of the adult heart, identify conserved pathways that regulate structural complexity, and reveal theirinfluence on susceptibility to disease

  • Journal article
    Chen J, Wang Z, Zhu T, Rosas FEet al., 2020,

    Recommendation algorithm in double-layer network based on vector dynamic evolution clustering and attention mechanism

    , Complexity, Vol: 2020, Pages: 1-19, ISSN: 1076-2787

    The purpose of recommendation systems is to help users find effective information quickly and conveniently and also to present the items that users are interested in. While the literature of recommendation algorithms is vast, most collaborative filtering recommendation approaches attain low recommendation accuracies and are also unable to track temporal changes of preferences. Additionally, previous differential clustering evolution processes relied on a single-layer network and used a single scalar quantity to characterise the status values of users and items. To address these limitations, this paper proposes an effective collaborative filtering recommendation algorithm based on a double-layer network. This algorithm is capable of fully exploring dynamical changes of user preference over time and integrates the user and item layers via an attention mechanism to build a double-layer network model. Experiments on Movielens, CiaoDVD, and Filmtrust datasets verify the effectiveness of our proposed algorithm. Experimental results show that our proposed algorithm can attain a better performance than other state-of-the-art algorithms.

  • Conference paper
    Rosas De Andraca FE, Azari M, Arani A, 2020,

    Mobile Cellular-Connected UAVs: Reinforcement Learning for Sky Limits

    , IEEE Globecom Workshops 2020
  • Conference paper
    Chen C, Qin C, Qiu H, Ouyang C, Wang S, Chen L, Tarroni G, Bai W, Rueckert Det al., 2020,

    Realistic adversarial data augmentation for MR image segmentation

    , International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)

    Neural network-based approaches can achieve high accuracy in various medicalimage segmentation tasks. However, they generally require large labelleddatasets for supervised learning. Acquiring and manually labelling a largemedical dataset is expensive and sometimes impractical due to data sharing andprivacy issues. In this work, we propose an adversarial data augmentationmethod for training neural networks for medical image segmentation. Instead ofgenerating pixel-wise adversarial attacks, our model generates plausible andrealistic signal corruptions, which models the intensity inhomogeneities causedby a common type of artefacts in MR imaging: bias field. The proposed methoddoes not rely on generative networks, and can be used as a plug-in module forgeneral segmentation networks in both supervised and semi-supervised learning.Using cardiac MR imaging we show that such an approach can improve thegeneralization ability and robustness of models as well as provide significantimprovements in low-data scenarios.

  • Journal article
    Fernando S, Scott-Brown J, Şerban O, Birch D, Akroyd D, Molina-Solana M, Heinis T, Guo Yet al., 2020,

    Open Visualization Environment (OVE): A web framework for scalable rendering of data visualizations

    , Future Generation Computer Systems, Vol: 112, Pages: 785-799, ISSN: 0167-739X

    Scalable resolution display environments, including immersive data observatories, are emerging as equitable and socially engaging platforms for collaborative data exploration and decision making. These environments require specialized middleware to drive them, but, due to various limitations, there is still a gap in frameworks capable of scalable rendering of data visualizations. To overcome these limitations, we introduce a new modular open-source middleware, the Open Visualization Environment (OVE). This framework uses web technologies to provide an ecosystem for visualizing data using web browsers that span hundreds of displays. In this paper, we discuss the key design features and architecture of our framework as well as its limitations. This is followed by an extensive study on performance and scalability, which validates its design and compares it to the popular SAGE2 middleware. We show how our framework solves three key limitations in SAGE2. Thereafter, we present two of our projects that used OVE and show how it can extend SAGE2 to overcome limitations and simplify the user experience for common data visualization use-cases.

  • Journal article
    Biffi C, Cerrolaza Martinez JJ, Tarroni G, Bai W, Simoes Monteiro de Marvao A, Oktay O, Ledig C, Le Folgoc L, Kamnitsas K, Doumou G, Duan J, Prasad S, Cook S, O'Regan D, Rueckert Det al., 2020,

    Explainable anatomical shape analysis through deep hierarchical generative models

    , IEEE Transactions on Medical Imaging, Vol: 39, Pages: 2088-2099, ISSN: 0278-0062

    Quantification of anatomical shape changes currently relies on scalar global indexes which are largely insensitive to regional or asymmetric modifications. Accurate assessment of pathology-driven anatomical remodeling is a crucial step for the diagnosis and treatment of many conditions. Deep learning approaches have recently achieved wide success in the analysis of medical images, but they lack interpretability in the feature extraction and decision processes. In this work, we propose a new interpretable deep learning model for shape analysis. In particular, we exploit deep generative networks to model a population of anatomical segmentations through a hierarchy of conditional latent variables. At the highest level of this hierarchy, a two-dimensional latent space is simultaneously optimised to discriminate distinct clinical conditions, enabling the direct visualisation of the classification space. Moreover, the anatomical variability encoded by this discriminative latent space can be visualised in the segmentation space thanks to the generative properties of the model, making the classification task transparent. This approach yielded high accuracy in the categorisation of healthy and remodelled left ventricles when tested on unseen segmentations from our own multi-centre dataset as well as in an external validation set, and on hippocampi from healthy controls and patients with Alzheimer’s disease when tested on ADNI data. More importantly, it enabled the visualisation in three-dimensions of both global and regional anatomical features which better discriminate between the conditions under exam. The proposed approach scales effectively to large populations, facilitating highthroughput analysis of normal anatomy and pathology in largescale studies of volumetric imaging.

  • Journal article
    Chen C, Bai W, Davies R, Bhuva A, Manisty C, Moon J, Aung N, Lee A, Sanghvi M, Fung K, Paiva J, Petersen S, Lukaschuk E, Piechnik S, Neubauer S, Rueckert Det al., 2020,

    Improving the generalizability of convolutional neural network-based segmentation on CMR images

    , Frontiers in Cardiovascular Medicine, ISSN: 2297-055X
  • Journal article
    Martínez V, Fernando S, Molina-Solana M, Guo Yet al., 2020,

    Tuoris: A middleware for visualizing dynamic graphics in scalable resolution display environments

    , Future Generation Computer Systems, Vol: 106, Pages: 559-571, ISSN: 0167-739X

    In the era of big data, large-scale information visualization has become an important challenge. Scalable resolution display environments (SRDEs) have emerged as a technological solution for building high-resolution display systems by tiling lower resolution screens. These systems bring serious advantages, including lower construction cost and better maintainability compared to other alternatives. However, they require specialized software but also purpose-built content to suit the inherently complex underlying systems. This creates several challenges when designing visualizations for big data, such that can be reused across several SRDEs of varying dimensions. This is not yet a common practice but is becoming increasingly popular among those who engage in collaborative visual analytics in data observatories. In this paper, we define three key requirements for systems suitable for such environments, point out limitations of existing frameworks, and introduce Tuoris, a novel open-source middleware for visualizing dynamic graphics in SRDEs. Tuoris manages the complexity of distributing and synchronizing the information among different components of the system, eliminating the need for purpose-built content. This makes it possible for users to seamlessly port existing graphical content developed using standard web technologies, and simplifies the process of developing advanced, dynamic and interactive web applications for large-scale information visualization. Tuoris is designed to work with Scalable Vector Graphics (SVG), reducing bandwidth consumption and achieving high frame rates in visualizations with dynamic animations. It scales independent of the display wall resolution and contrasts with other frameworks that transmit visual information as blocks of images.

  • Journal article
    Ali MK, Kim RY, Brown AC, Mayall JR, Karim R, Pinkerton JW, Liu G, Martin KL, Starkey MR, Pillar A, Donovan C, Pathinayake PS, Carroll OR, Trinder D, Tay HL, Badi YE, Kermani NZ, Guo Y-K, Aryal R, Mumby S, Pavlidis S, Adcock IM, Weaver J, Xenaki D, Oliver BG, Holliday EG, Foster PS, Wark PA, Johnstone DM, Milward EA, Hansbro PM, Horvat JCet al., 2020,

    Crucial role for lung iron level and regulation in the pathogenesis and severity of asthma.

    , European Respiratory Journal, Vol: 55, Pages: 1-14, ISSN: 0903-1936

    Accumulating evidence highlights links between iron regulation and respiratory disease. Here, we assessed the relationship between iron levels and regulatory responses in clinical and experimental asthma.We show that cell-free iron levels are reduced in the bronchoalveolar lavage (BAL) supernatant of severe or mild-moderate asthma patients and correlate with lower forced expiratory volume in 1 s (FEV1). Conversely, iron-loaded cell numbers were increased in BAL in these patients and with lower FEV1/forced vital capacity (FEV1/FVC). The airway tissue expression of the iron sequestration molecules divalent metal transporter 1 (DMT1) and transferrin receptor 1 (TFR1) are increased in asthma with TFR1 expression correlating with reduced lung function and increased type 2 (T2) inflammatory responses in the airways. Furthermore, pulmonary iron levels are increased in a house dust mite (HDM)-induced model of experimental asthma in association with augmented Tfr1 expression in airway tissue, similar to human disease. We show that macrophages are the predominant source of increased Tfr1 and Tfr1+ macrophages have increased Il13 expression. We also show that increased iron levels induce increased pro-inflammatory cytokine and/or extracellular matrix (ECM) responses in human airway smooth muscle (ASM) cells and fibroblasts ex vivo and induce key features of asthma, including airway hyper-responsiveness and fibrosis and T2 inflammatory responses, in vivoTogether these complementary clinical and experimental data highlight the importance of altered pulmonary iron levels and regulation in asthma, and the need for a greater focus on the role and potential therapeutic targeting of iron in the pathogenesis and severity of disease.

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