Publications
203 results found
Lino M, Fotiadis S, Bharath AA, et al., 2023, Current and emerging deep-learning methods for the simulation of fluid dynamics, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol: 479, Pages: 1-39, ISSN: 1364-5021
Over the last decade, deep learning (DL), a branch of machine learning, has experienced rapid progress. Powerful tools for tasks that have been traditionally complex to automate have been developed, such as image synthesis and natural language processing. In the context of simulating fluid dynamics, this has led to a series of novel DL methods for replacing or augmenting conventional numerical solvers. We broadly classify these methods into physics- and data-driven methods. Physics-driven methods, generally, tune a DL model to provide an analytical and differentiable solution to a given fluid dynamics problem by minimizing the residuals of the governing partial differential equations. Data-driven methods provide a fast and approximate solution to any fluid dynamics problem that shares some physical properties with the observations used when tuning the DL model’s parameters. Meanwhile, the symbiosis of numerical solvers and DL has led to promising results in turbulence modelling and accelerating iterative solvers. However, these methods present some challenges. Exclusively data-driven flow simulators often suffer from poor extrapolation, error accumulation in time-dependent simulations, as well as difficulties in training against turbulent flows. Substantial effort is, therefore, being invested into approaches that may improve the current state of the art.
Zaman S, Vimalesvaran K, Howard JP, et al., 2023, Efficient labelling for efficient deep learning: the benefit of a multiple-image-ranking method to generate high volume training data applied to ventricular slice level classification in cardiac MRI., J Med Artif Intell, Vol: 6
BACKGROUND: Getting the most value from expert clinicians' limited labelling time is a major challenge for artificial intelligence (AI) development in clinical imaging. We present a novel method for ground-truth labelling of cardiac magnetic resonance imaging (CMR) image data by leveraging multiple clinician experts ranking multiple images on a single ordinal axis, rather than manual labelling of one image at a time. We apply this strategy to train a deep learning (DL) model to classify the anatomical position of CMR images. This allows the automated removal of slices that do not contain the left ventricular (LV) myocardium. METHODS: Anonymised LV short-axis slices from 300 random scans (3,552 individual images) were extracted. Each image's anatomical position relative to the LV was labelled using two different strategies performed for 5 hours each: (I) 'one-image-at-a-time': each image labelled according to its position: 'too basal', 'LV', or 'too apical' individually by one of three experts; and (II) 'multiple-image-ranking': three independent experts ordered slices according to their relative position from 'most-basal' to 'most apical' in batches of eight until each image had been viewed at least 3 times. Two convolutional neural networks were trained for a three-way classification task (each model using data from one labelling strategy). The models' performance was evaluated by accuracy, F1-score, and area under the receiver operating characteristics curve (ROC AUC). RESULTS: After excluding images with artefact, 3,323 images were labelled by both strategies. The model trained using labels from the 'multiple-image-ranking strategy' performed better than the model using the 'one-image-at-a-time' labelling strategy (accuracy 86% vs. 72%, P=0.02; F1-score 0.86 vs. 0.75; ROC AUC 0.95 vs. 0.86). For expert clinicians performing this task manually the intra-observer variability was low (Cohen's κ=0.90), but the inter-observer variability was higher (Cohen's &kap
Atchison C, Moshe M, Brown J, et al., 2023, Validity of self-testing at home with rapid SARS-CoV-2 antibody detection by lateral flow immunoassay, Clinical Infectious Diseases, Vol: 76, Pages: 658-666, ISSN: 1058-4838
Background: We explore severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibody lateral flow immunoassay (LFIA) performance under field conditions compared to laboratory-based ELISA and live virus neutralisation. Methods: In July 2021, 3758 participants performed, at home, a self-administered LFIA on finger-prick blood, reported and submitted a photograph of the result, and provided a self-collected capillary blood sample for assessment of IgG antibodies using the Roche Elecsys® Anti-SARS-CoV-2 assay. We compared the self-reported LFIA result to the quantitative Roche assay and checked the reading of the LFIA result with an automated image analysis (ALFA). In a subsample of 250 participants, we compared the results to live virus neutralisation. Results: Almost all participants (3593/3758, 95.6%) had been vaccinated or reported prior infection. Overall, 2777/3758 (73.9%) were positive on self-reported LFIA, 2811/3457 (81.3%) positive by LFIA when ALFA-reported, and 3622/3758 (96.4%) positive on Roche (using the manufacturer reference standard threshold for positivity of 0.8 U ml−1). Live virus neutralisation was detected in 169 of 250 randomly selected samples (67.6%); 133/169 were positive with self-reported LFIA (sensitivity 78.7%; 95% CI 71.8, 84.6), 142/155 (91.6%; 86.1, 95.5) with ALFA, and 169 (100%; 97.8, 100.0) with Roche. There were 81 samples with no detectable virus neutralisation; 47/81 were negative with self-reported LFIA (specificity 58.0%; 95% CI 46.5, 68.9), 34/75 (45.3%; 33.8, 57.3) with ALFA, and 0/81 (0%; 0.0, 4.5) with Roche. Conclusions: Self-administered LFIA is less sensitive than a quantitative antibody test, but the positivity in LFIA correlates better than the quantitative ELISA with virus neutralisation.
Uslu F, Bharath AA, 2023, TMS-Net: A segmentation network coupled with a run-time quality control method for robust cardiac image segmentation, COMPUTERS IN BIOLOGY AND MEDICINE, Vol: 152, ISSN: 0010-4825
Varela M, Bharath AA, 2023, Prototype of a Cardiac MRI Simulator for the Training of Supervised Neural Networks, Functional Imaging and Modeling of the Heart, Publisher: Springer Nature Switzerland, Pages: 366-374, ISBN: 9783031353017
Gionfrida L, Rusli WMRB, Bharath A, et al., 2022, Validation of two-dimensional video-based inference of finger kinematics with pose estimation, PLoS One, Vol: 17, ISSN: 1932-6203
Accurate capture finger of movements for biomechanical assessments has typically been achieved within laboratory environments through the use of physical markers attached to a participant’s hands. However, such requirements can narrow the broader adoption of movement tracking for kinematic assessment outside these laboratory settings, such as in the home. Thus, there is the need for markerless hand motion capture techniques that are easy to use and accurate enough to evaluate the complex movements of the human hand. Several recent studies have validated lower-limb kinematics obtained with a marker-free technique, OpenPose. This investigation examines the accuracy of OpenPose, when applied to images from single RGB cameras, against a ‘gold standard’ marker-based optical motion capture system that is commonly used for hand kinematics estimation. Participants completed four single-handed activities with right and left hands, including hand abduction and adduction, radial walking, metacarpophalangeal (MCP) joint flexion, and thumb opposition. The accuracy of finger kinematics was assessed using the root mean square error. Mean total active flexion was compared using the Bland–Altman approach, and the coefficient of determination of linear regression. Results showed good agreement for abduction and adduction and thumb opposition activities. Lower agreement between the two methods was observed for radial walking (mean difference between the methods of 5.03°) and MCP flexion (mean difference of 6.82°) activities, due to occlusion. This investigation demonstrated that OpenPose, applied to videos captured with monocular cameras, can be used for markerless motion capture for finger tracking with an error below 11° and on the order of that which is accepted clinically.
Gionfrida L, Bharath A, Kedgley A, et al., 2022, Validation of two-dimensional video-based inference of finger kinematics with pose estimation., PLoS One, ISSN: 1932-6203
Vimalesvaran K, Uslu F, Zaman S, et al., 2022, Machine learning can accurately detect abnormal aortic valves in CMR, Publisher: OXFORD UNIV PRESS, Pages: 236-236, ISSN: 0195-668X
Gionfrida L, Rusli W, Kedgley A, et al., 2022, A 3DCNN-LSTM multi-class temporal segmentation for hand gesture recognition, Electronics, Vol: 11, ISSN: 2079-9292
This paper introduces a multi-class hand gesture recognition model developed to identify a set of hand gesture sequences from two-dimensional RGB video recordings, using both the appearance and spatiotemporal parameters of consecutive frames. The classifier utilizes a convolutional-based network combined with a long-short-term memory unit. To leverage the need for a large-scale dataset, the model deploys training on a public dataset, adopting a technique known as transfer learning to fine-tune the architecture on the hand gestures of relevance. Validation curves performed over a batch size of 64 indicate an accuracy of 93.95% (±0.37) with a mean Jaccard index of 0.812 (±0.105) for 22 participants. The fine-tuned architecture illustrates the possibility of refining a model with a small set of data (113,410 fully labelled image frames) to cover previously unknown hand gestures. The main contribution of this work includes a custom hand gesture recognition network driven by monocular RGB video sequences that outperform previous temporal segmentation models, embracing a small-sized architecture that facilitates wide adoption.
Gionfrida L, Rusli WMR, Kedgley AE, et al., 2022, A 3DCNN-LSTM Multi-Class Temporal Segmentation for Hand Gesture Recognition, Electronics, Vol: 11, Pages: 2427-2427
<jats:p>This paper introduces a multi-class hand gesture recognition model developed to identify a set of hand gesture sequences from two-dimensional RGB video recordings, using both the appearance and spatiotemporal parameters of consecutive frames. The classifier utilizes a convolutional-based network combined with a long-short-term memory unit. To leverage the need for a large-scale dataset, the model deploys training on a public dataset, adopting a technique known as transfer learning to fine-tune the architecture on the hand gestures of relevance. Validation curves performed over a batch size of 64 indicate an accuracy of 93.95% (±0.37) with a mean Jaccard index of 0.812 (±0.105) for 22 participants. The fine-tuned architecture illustrates the possibility of refining a model with a small set of data (113,410 fully labelled image frames) to cover previously unknown hand gestures. The main contribution of this work includes a custom hand gesture recognition network driven by monocular RGB video sequences that outperform previous temporal segmentation models, embracing a small-sized architecture that facilitates wide adoption.</jats:p>
Lino M, Fotiadis S, Bharath AA, et al., 2022, Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics, PHYSICS OF FLUIDS, Vol: 34, ISSN: 1070-6631
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Lino M, Cantwell C, Fotiadis S, et al., 2022, REMuS-GNN: A rotation-equivariant model for simulating continuum dynamics, Algebraic and Geometric Learning Workshops 2022, Publisher: ML Research Press, Pages: 226-236
Numerical simulation is an essential tool in many areas of science and engineering, but its performance often limits application in practice or when used to explore large parameter spaces. On the other hand, surrogate deep learning models, while accelerating simulations, often exhibit poor accuracy and ability to generalise. In order to improve these two factors, we introduce REMuS-GNN, a rotation-equivariant multi-scale model for simulating continuum dynamical systems encompassing a range of length scales. REMuS-GNN is designed to predict an output vector field from an input vector field on a physical domain discretised into an unstructured set of nodes. Equivariance to rotations of the domain is a desirable inductive bias that allows the network to learn the underlying physics more efficiently, leading to improved accuracy and generalisation compared with similar architectures that lack such symmetry. We demonstrate and evaluate this method on the incompressible flow around elliptical cylinders.
Wong N, Meshkinfamfard S, Turbé V, et al., 2022, Machine learning to support visual auditing of home-based lateral flow immunoassay self-test results for SARS-CoV-2 antibodies, Communications Medicine, Vol: 2, ISSN: 2730-664X
Lateral flow immunoassays (LFIAs) are being used worldwide for COVID-19 mass testing and antibody prevalence studies. Relatively simple to use and low cost, these tests can be self-administered at home but rely on subjective interpretation of a test line by eye, risking false positives and negatives. Here we report the development of ALFA (Automated Lateral Flow Analysis) to improve reported sensitivity and specificity. Our computational pipeline uses machine learning, computer vision techniques and signal processing algorithms to analyse images of the Fortress LFIA SARS-CoV-2 antibody self-test, and subsequently classify results as invalid, IgG negative and IgG positive. A large image library of 595,339 participant-submitted test photographs was created as part of the REACT-2 community SARS-CoV-2 antibody prevalence study in England, UK. Automated analysis showed substantial agreement with human experts (Kappa 0.90-0.97) and performed consistently better than study participants, particularly for weak positive IgG results. Specificity (98.7-99.4%) and sensitivity (90.1-97.1%) were high compared with visual interpretation by human experts (ranges due to the varying prevalence of weak positive IgG tests in datasets). Alongside ALFA, we developed an analysis toolkit which could also detect device blood leakage issues. Given the potential for LFIAs to be used at scale in the COVID-19 response (for both antibody and antigen testing), even a small improvement in the accuracy of the algorithms could impact the lives of millions of people by reducing the risk of false positive and false negative result read-outs by members of the public. Our findings support the use of machine learning-enabled automated reading of at-home antibody lateral flow tests, to be a tool for improved accuracy for population-level community surveillance.
Lino M, Fotiadis S, Bharath AA, et al., 2022, Towards fast simulation of environmental fluid mechanics with multi-scale graph neural networks, AI for Earth and Space Science, Publisher: ICLR, Pages: 1-11
Numerical simulators are essential tools in the study of naturalfluid-systems, but their performance often limits application in practice.Recent machine-learning approaches have demonstrated their ability toaccelerate spatio-temporal predictions, although, with only moderate accuracyin comparison. Here we introduce MultiScaleGNN, a novel multi-scale graphneural network model for learning to infer unsteady continuum mechanics inproblems encompassing a range of length scales and complex boundary geometries.We demonstrate this method on advection problems and incompressible fluiddynamics, both fundamental phenomena in oceanic and atmospheric processes. Ourresults show good extrapolation to new domain geometries and parameters forlong-term temporal simulations. Simulations obtained with MultiScaleGNN arebetween two and four orders of magnitude faster than those on which it wastrained.
Lino M, Fotiadis S, Bharath AA, et al., 2022, REMuS-GNN: A rotation-equivariant model for simulating continuum dynamics, ICLR 2022 workshop on ‘Geometrical and Topological Representation Learning’, Publisher: OpenReview.net
Numerical simulation is an essential tool in many areas of science and engineering, but its performance often limits application in practice, or when used to explore large parameter spaces. On the other hand, surrogate deep learning models, while accelerating simulations, often exhibit poor accuracy and ability to generalise. In order to improve these two factors, we introduce REMuS-GNN, a rotation-equivariant multi-scale model for simulating continuum dynamical systems encompassing a range of length scales. REMuS-GNN is designed to predict an output vector field from an input vector field on a physical domain discredited into an unstructured set of nodes. Equivariance to rotations of the domain is a desirable inductive bias that allows the network to learn the underlying physics more efficiently, leading to improved accuracy and generalisation compared with similar architectures that lack such symmetry. We demonstrate and evaluate this method on the incompressible flow around elliptical cylinders.
Bharath A, Uslu F, Varela Anjari M, et al., 2022, LA-Net: A multi-task deep network for the segmentation of the left atrium, IEEE Transactions on Medical Imaging, Vol: 41, Pages: 456-464, ISSN: 0278-0062
Although atrial fibrillation (AF) is the most common sustained atrial arrhythmia, treatment success for this condition remains suboptimal. Information from magnetic resonance imaging (MRI) has the potential to improve treatment efficacy, but there are currently few automatic tools for the segmentation of the atria in MR images. In the study, we propose a LA-Net, a multi-task network optimised to simultaneously generate left atrial segmentation and edge masks from MRI. LA-Net includes cross attention modules (CAMs) and enhanced decoder modules (EDMs) to purposefully select the most meaningful edge information for segmentation and smoothly incorporate it into segmentation masks at multiple-scales. We evaluate the performance of LA-Net on two MR sequences: late gadolinium enhanced (LGE) atrial MRI and atrial short axis balanced steady state free precession (bSSFP) MRI. LA-Net gives Hausdorff distances of 12.43 mm and Dice scores of 0.92 on the LGE (STACOM 2018) dataset and Hausdorff distances of 17.41 mm and Dice scores of 0.90 on the bSSFP (in-house) dataset without any post-processing, surpassing previously proposed segmentation networks, including U-Net and SEGANet. Our method allows automatic extraction of information about the LA from MR images, which can play an important role in the management of AF patients.
Dai T, Du Y, Fang M, et al., 2022, Diversity-augmented intrinsic motivation for deep reinforcement learning, Neurocomputing, Vol: 468, Pages: 396-406, ISSN: 0925-2312
In many real-world problems, reward signals received by agents are delayed or sparse, which makes it challenging to train a reinforcement learning (RL) agent. An intrinsic reward signal can help an agent to explore such environments in the quest for novel states. In this work, we propose a general end-to-end diversity-augmented intrinsic motivation for deep reinforcement learning which encourages the agent to explore new states and automatically provides denser rewards. Specifically, we measure the diversity of adjacent states under a model of state sequences based on determinantal point process (DPP); this is coupled with a straight-through gradient estimator to enable end-to-end differentiability. The proposed approach is comprehensively evaluated on the MuJoCo and the Arcade Learning Environments (Atari and SuperMarioBros). The experiments show that an intrinsic reward based on the diversity measure derived from the DPP model accelerates the early stages of training in Atari games and SuperMarioBros. In MuJoCo, the approach improves on prior techniques for tasks using the standard reward setting, and achieves the state-of-the-art performance on 12 out of 15 tasks containing delayed rewards.
Zaydullin R, Bharath AA, Grisan E, et al., 2022, Motion Correction Using Deep Learning Neural Networks - Effects of Data Representation, IEEE International Ultrasonics Symposium (IUS), Publisher: IEEE, ISSN: 1948-5719
Vimalesvaran K, Uslu F, Zaman S, et al., 2022, Detecting Aortic Valve Pathology from the 3-Chamber Cine Cardiac MRI View, Editors: Wang, Dou, Fletcher, Speidel, Li, Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 571-580, ISBN: 978-3-031-16430-9
Zaman S, Petri C, Vimalesvaran K, et al., 2022, Automatic diagnosis labeling of cardiovascular MRI by using semisupervised natural language processing of text reports, Radiology: Artificial Intelligence, Vol: 4, ISSN: 2638-6100
A semisupervised natural language processing (NLP) algorithm, based on bidirectional transformers, accurately categorized diagnoses from cardiac MRI text of radiology reports for the labeling of MR images; the model had a higher accuracy than traditional NLP models and performed faster labeling than clinicians.
Galazis C, Wu H, Li Z, et al., 2022, Tempera: spatial transformer feature pyramid network for cardiac MRI segmentation, 12th International Workshop, STACOM 2021, Publisher: Springer International Publishing, Pages: 268-276, ISSN: 0302-9743
Assessing the structure and function of the right ventricle (RV) is important in the diagnosis of several cardiac pathologies. However, it remains more challenging to segment the RV than the left ventricle (LV). In this paper, we focus on segmenting the RV in both short (SA) and long-axis (LA) cardiac MR images simultaneously. For this task, we propose a new multi-input/output architecture, hybrid 2D/3D geometric spatial TransformEr Multi-Pass fEature pyRAmid (Tempera). Our feature pyramid extends current designs by allowing not only a multi-scale feature output but multi-scale SA and LA input images as well. Tempera transfers learned features between SA and LA images via layer weight sharing and incorporates a geometric target transformer to map the predicted SA segmentation to LA space. Our model achieves an average Dice score of 0.836 and 0.798 for the SA and LA, respectively, and 26.31 mm and 31.19 mm Hausdorff distances. This opens up the potential for the incorporation of RV segmentation models into clinical workflows.
Herrero Martin C, Oved A, Chowdhury R, et al., 2021, EP-PINNs: cardiac electrophysiology characterisation using physics-informed neural networks, Frontiers in Cardiovascular Medicine, ISSN: 2297-055X
Accurately inferring underlying electrophysiological (EP) tissue properties from action potential recordings is expected to be clinically useful in the diagnosis and treatment of arrhythmias such as atrial fibrillation, but it is notoriously difficult to perform. We present EP-PINNs (Physics-Informed Neural Networks), a novel tool for accurate action potential simulation and EP parameter estimation, from sparse amounts of EP data. We demonstrate, using 1D and 2D in silico data, how EP-PINNs are able to reconstruct the spatio-temporal evolution of action potentials, whilst predicting parameters related to action potential duration (APD), excitability and diffusion coefficients. EP-PINNs are additionally able to identify heterogeneities in EP properties, making them potentially useful for the detection of fibrosis and other localised pathology linked to arrhythmias. Finally, we show EP-PINNs effectiveness on biological in vitro preparations, by characterising the effect of anti-arrhythmic drugs on APD using optical mapping data. EP-PINNs are a promising clinical tool for the characterisation and potential treatment guidance of arrhythmias.
Martin CH, Oved A, Chowdhury RA, et al., 2021, EP-PINNs: cardiac electrophysiology characterisation using physics-informed neural networks, Publisher: arXiv
Accurately inferring underlying electrophysiological (EP) tissue propertiesfrom action potential recordings is expected to be clinically useful in thediagnosis and treatment of arrhythmias such as atrial fibrillation, but it isnotoriously difficult to perform. We present EP-PINNs (Physics-Informed NeuralNetworks), a novel tool for accurate action potential simulation and EPparameter estimation, from sparse amounts of EP data. We demonstrate, using 1Dand 2D in silico data, how EP-PINNs are able to reconstruct the spatio-temporalevolution of action potentials, whilst predicting parameters related to actionpotential duration (APD), excitability and diffusion coefficients. EP-PINNs areadditionally able to identify heterogeneities in EP properties, making thempotentially useful for the detection of fibrosis and other localised pathologylinked to arrhythmias. Finally, we show EP-PINNs effectiveness on biological invitro preparations, by characterising the effect of anti-arrhythmic drugs onAPD using optical mapping data. EP-PINNs are a promising clinical tool for thecharacterisation and potential treatment guidance of arrhythmias.
Dai T, Liu H, Arulkumaran K, et al., 2021, Diversity-based trajectory and goal selection with hindsight experience replay, 18th Pacific Rim International Conference on Artificial Intelligence (PRICAI), Publisher: Springer, Pages: 32-45
Hindsight experience replay (HER) is a goal relabelling technique typicallyused with off-policy deep reinforcement learning algorithms to solvegoal-oriented tasks; it is well suited to robotic manipulation tasks thatdeliver only sparse rewards. In HER, both trajectories and transitions aresampled uniformly for training. However, not all of the agent's experiencescontribute equally to training, and so naive uniform sampling may lead toinefficient learning. In this paper, we propose diversity-based trajectory andgoal selection with HER (DTGSH). Firstly, trajectories are sampled according tothe diversity of the goal states as modelled by determinantal point processes(DPPs). Secondly, transitions with diverse goal states are selected from thetrajectories by using k-DPPs. We evaluate DTGSH on five challenging roboticmanipulation tasks in simulated robot environments, where we show that ourmethod can learn more quickly and reach higher performance than otherstate-of-the-art approaches on all tasks.
Patel R, Thong EHE, Batta V, et al., 2021, Automated Identification of Orthopedic Implants on Radiographs Using Deep Learning, RADIOLOGY-ARTIFICIAL INTELLIGENCE, Vol: 3, ISSN: 2638-6100
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Liu Y, Zou Z, Yang Y, et al., 2021, Efficient Source Camera Identification with Diversity-Enhanced Patch Selection and Deep Residual Prediction, SENSORS, Vol: 21
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Rodrigues J, Bharath A, Overby D, 2021, Automated machine learning detection of transcellular pores in Schlemm's canal endothelial cells exposed to stretch, Publisher: ASSOC RESEARCH VISION OPHTHALMOLOGY INC, ISSN: 0146-0404
Davis BM, Guo L, Ravindran N, et al., 2020, Dynamic changes in cell size and corresponding cell fate after optic nerve injury, Scientific Reports, Vol: 10, ISSN: 2045-2322
Identifying disease-specific patterns of retinal cell loss in pathological conditions has been highlighted by the emergence of techniques such as Detection of Apoptotic Retinal Cells and Adaptive Optics confocal Scanning Laser Ophthalmoscopy which have enabled single-cell visualisation in vivo. Cell size has previously been used to stratify Retinal Ganglion Cell (RGC) populations in histological samples of optic neuropathies, and early work in this field suggested that larger RGCs are more susceptible to early loss than smaller RGCs. More recently, however, it has been proposed that RGC soma and axon size may be dynamic and change in response to injury. To address this unresolved controversy, we applied recent advances in maximising information extraction from RGC populations in retinal whole mounts to evaluate the changes in RGC size distribution over time, using three well-established rodent models of optic nerve injury. In contrast to previous studies based on sampling approaches, we examined the whole Brn3a-positive RGC population at multiple time points over the natural history of these models. The morphology of over 4 million RGCs was thus assessed to glean novel insights from this dataset. RGC subpopulations were found to both increase and decrease in size over time, supporting the notion that RGC cell size is dynamic in response to injury. However, this study presents compelling evidence that smaller RGCs are lost more rapidly than larger RGCs despite the dynamism. Finally, using a bootstrap approach, the data strongly suggests that disease-associated changes in RGC spatial distribution and morphology could have potential as novel diagnostic indicators.
Lino M, Cantwell C, Fotiadis S, et al., 2020, Simulating surface wave dynamics with convolutional networks, Publisher: arXiv
We investigate the performance of fully convolutional networks to simulatethe motion and interaction of surface waves in open and closed complexgeometries. We focus on a U-Net architecture and analyse how well itgeneralises to geometric configurations not seen during training. Wedemonstrate that a modified U-Net architecture is capable of accuratelypredicting the height distribution of waves on a liquid surface within curvedand multi-faceted open and closed geometries, when only simple box andright-angled corner geometries were seen during training. We also consider aseparate and independent 3D CNN for performing time-interpolation on thepredictions produced by our U-Net. This allows generating simulations with asmaller time-step size than the one the U-Net has been trained for.
Lourenco A, Kerfoot E, Dibblin C, et al., 2020, Automatic estimation of left atrial function from short axis CINE-MRI using machine learning, European-Society-of-Cardiology (ESC) Congress, Publisher: OXFORD UNIV PRESS, Pages: 229-229, ISSN: 0195-668X
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