Retina vessel width estimation using bifurcation points to track vessels

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Lightweight deep learning models for detecting COVID-19 from chest X-ray images

Funding Information: The authors would like to thank the multiple teams that have contributed to the release of the datasets used in this paper. We would also like to thank the Data Lab, which provided an MSc AI scholarship to the first author, making this project possible.Peer reviewedPreprintPostprin

Fully Homomorphically Encrypted Deep Learning as a Service

Funding: This research was funded by UKRI-EPSRC grant “The Internet of Food Things” grant number EP/R045127/1.Peer reviewedPublisher PD

Contrastive Domain Adaptation

AUTHOR ONLINE USE Personal Servers. Authors and/or their employers shall have the right to post the accepted version of IEEE-copyrighted articles on their own personal servers or the servers of their institutions or employers without permission from IEEE, provided that the posted version includes a prominently displayed IEEE copyright notice and, when published, a full citation to the original IEEE publication, including a link to the article abstract in IEEE Xplore. Authors shall not post the final, published versions of their papers."Publisher PD

Introducing Multi-Source Domain Adaptation for Quality Control in Retail Food Packaging

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Early screening and diagnosis of diabetic retinopathy

Diabetic retinopathy (DR) is a chronic, progressive and possibly vision-threatening eye disease. Early detection and diagnosis of DR, prior to the development of any lesions, is paramount for more efficiently dealing with it and managing its consequences. This thesis investigates and proposes a number of candidate geometric and haemodynamic biomarkers, derived from fundus images of the retinal vasculature, which can be reliably utilised for identifying the progression from diabetes to DR. Numerous studies exist in literature that investigate only some of these biomarkers in independent normal, diabetic and DR cohorts. However, none exist, to the best of my knowledge, that investigates more than 100 biomarkers altogether, both geometric and haemodynamic ones, for identifying the progression to DR, by also using a novel experimental design, where the same exact matched junctions and subjects are evaluated in a four year period that includes the last three years pre-DR (still diabetic eye) and the onset of DR (progressors’ group). Multiple additional conventional experimental designs, such as non-matched junctions, non-progressors’ group, and a combination of them are also adopted in order to present the superiority of this type of analysis for retinal features. Therefore, this thesis aims to present a complete framework and some novel knowledge, based on statistical analysis, feature selection processes and classification models, so as to provide robust, rigorous and meaningful statistical inferences, alongside efficient feature subsets that can identify the stages of the progression. In addition, a new and improved method for more accurately summarising the calibres of the retinal vessel trunks is also presented. The first original contribution of this thesis is that a series of haemodynamic features (blood flow rate, blood flow velocity, etc.), which are estimated from the retinal vascular geometry based on some boundary conditions, are applied to studying the progression from diabetes to DR. These features are found to undoubtedly contribute to the inferences and the understanding of the progression, yielding significant results, mainly for the venular network. The second major contribution is the proposed framework and the experimental design for more accurately and efficiently studying and quantifying the vascular alterations that occur during the progression to DR and that can be safely attributed only to this progression. The combination of the framework and the experimental design lead to more sound and concrete inferences, providing a set of features, such as the central retinal artery and vein equivalent, fractal dimension, blood flow rate, etc., that are indeed biomarkers of progression to DR. The third major contribution of this work is the new and improved method for more accurately summarising the calibre of an arterial or venular trunk, with a direct application to estimating the central retinal artery equivalent (CRAE), the central retinal vein equivalent (CRVE) and their quotient, the arteriovenous ratio (AVR). Finally, the improved method is shown to truly make a notable difference in the estimations, when compared to the established alternative method in literature, with an improvement between 0.24% and 0.49% in terms of the mean absolute percentage error and 0.013 in the area under the curve. I have demonstrated that some thoroughly planned experimental studies based on a comprehensive framework, which combines image processing algorithms, statistical and classification models, feature selection processes, and robust haemodynamic and geometric features, extracted from the retinal vasculature (as a whole and from specific areas of interest), provide altogether succinct evidence that the early detection of the progression from diabetes to DR can be indeed achieved. The performance that the eight different classification combinations achieved in terms of the area under the curve varied from 0.745 to 0.968

Hyperspherically Regularized Networks for Self-Supervision

This work used the Cirrus UK National Tier-2 HPC Service at EPCC (http://www.cirrus.ac.uk). Access granted through the project: ec173 - Next gen self-supervised learning systems for vision tasks.Preprin

Attention-Based Deep Learning Methods for Predicting Gas Turbine Emissions

This work was supported by the Engineering and Physical Sciences Research Council [EP/W522089/1].Peer reviewedPublisher PD

Retinal vascular geometry: novel biomarkers of progression from diabetes to diabetic retinopathy

Diabetic retinopathy (DR) remains a major cause of blindness in the developed countries. Geometric and Haemodynamic features are still not widely investigated, especially as biomarkers of progression to DR. Most studies rely on disease vs control design, which introduces errors and limitations, given the diversity of the retinal vascular geometry (small and large vessels). Our studies have mainly focused on investigating the vascular changes within the same patients during a four year period that includes the last three years of pre-DR and 1st year of DR (onset)

HMSN : Hyperbolic Self-Supervised Learning by Clustering with Ideal Prototypes

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