Marta Vallejo

• MSc, PhD
• Faculty Member at Heriot-Watt University

Early and accurate diagnosis of Parkinson's disease (PD) is essential for enabling timely treatment and effective disease management. In this study, we propose a deep learning approach to automate PD detection using convolutional neural networks (CNNs) trained on images derived from spiral drawing tasks performed by patients and healthy controls. T...

Amyotrophic lateral sclerosis (ALS) is a fatal neurological disease marked by motor deterioration and cognitive decline. Early diagnosis is challenging due to the complexity of sporadic ALS and the lack of a defined risk population. In this study, we developed Miniset-DenseSENet, a convolutional neural network combining DenseNet121 with a Squeeze-a...

Amyotrophic lateral sclerosis (ALS) is a fatal neurological disease characterized by motor deterioration and cognitive decline, leading to respiratory failure. Early diagnosis is crucial but challenging due to the undefined risk population and the complexity of sporadic ALS. In this study, we used a dataset of 190 autopsy brain images from the Greg...

Quantum metrology & sensing (QMS) and quantum enhanced imaging (QEI) are very promising in terms of commercial applications. Presently, however, both technologies face several roadblocks that need to be tackled in order to enable a broader commercialization: 1. Need for advanced and cost intensive laboratory equipment makes it difficult to spread q...

Label-free autofluorescence lifetime is a unique feature of the inherent fluorescence signals emitted by natural fluorophores in biological samples. Fluorescence lifetime imaging microscopy (FLIM) can capture these signals enabling comprehensive analyses of biological samples. Despite the fundamental importance and wide application of FLIM in biome...

Many medical imaging modalities have benefited from recent advances in Machine Learning (ML), specifically in deep learning, such as neural networks. Computers can be trained to investigate and enhance medical imaging methods without using valuable human resources. In recent years, Fluorescence Lifetime Imaging (FLIm) has received increasing attent...

Autofluorescence lifetime images reveal unique characteristics of endogenous fluorescence in biological samples. Comprehensive understanding and clinical diagnosis rely on co-registration with the gold standard, histology images, which is extremely challenging due to the difference of both images. Here, we show an unsupervised image-to-image transl...

In this paper, we introduce our unique dataset of fluorescence lifetime imaging endo/microscopy (FLIM), containing over 100,000 different FLIM images collected from 18 pairs of cancer/non-cancer human lung tissues of 18 patients by our custom fibre-based FLIM system. The aim of providing this dataset is that more researchers from relevant fields ca...

Autofluorescence lifetime images reveal unique characteristics of endogenous fluorescence in biological samples. Comprehensive understanding and clinical diagnosis rely on co-registration with the gold standard, histology images, which is extremely challenging due to the difference of both images. Here, we show an unsupervised image-to-image transl...

Parkinson’s disease (PD) is a progressive neurodegenerative disorder that causes abnormal movements and an array of other symptoms. An accurate PD diagnosis can be a challenging task as the signs and symptoms, particularly at an early stage, can be similar to other medical conditions or the physiological changes of normal ageing. This work aims to...

Parkinson’s disease is a disorder that affects the neurons in the human brain. It is characterized by various symptoms such as slowness of motor functions (bradykinesia), motor instability, speech impairment and, in some cases, psychiatric effects such as hallucinations. Most of the symptoms mentioned here, however, are also common side effects of...

Multi-scale architectures at a granular level are characterised by separating input features into groups and applying multi-scale feature extractions to the split input features, and thus the correlations among the input features as global information are no longer retained. Moreover, they usually require more input features due to the separation,...

Multispectral stem cells screening sensor technology "A colony of iPS cells obtained by reprogramming fibroblasts "

Due to the continued evolution of the SARS-CoV-2 pandemic, researchers worldwide are working to mitigate, suppress its spread, and better understand it by deploying digital signal processing (DSP) and machine learning approaches. This study presents an alignment-free approach to classify the SARS-CoV-2 using complementary DNA, which is DNA synthesi...

It is well understood in the field of microscopy that the visualization of the finest details was often limited by the ‘intrusion’ of light from out-of-focus planes. Biomedical research has driven the need for microscopes that can resolve very fine detail in three dimensions within intact, and often living, specimens. The use of fluorescence labell...

It is well understood in the field of microscopy that the visualization of the finest details was often limited by the ‘intrusion’ of light from out-of-focus planes. Biomedical research has driven the need for microscopes that can resolve very fine detail in three dimensions within intact, and often living, specimens. The use of fluorescence labell...

Deep learning technologies have been successfully applied to automatic diagnostics of ex-vivo lung cancer with fluorescence lifetime imaging endomicroscopy (FLIM). Recent advance in convolutional neural networks (CNNs) by splitting input features for multi-scale feature extraction as a feature-level aggregation, has achieved further improvement in...

Reinforcement learning problems are often discretised, use linear function approximation, or perform batch updates. However, many applications that can benefit from reinforcement learning contain continuous variables and are inherently non-linear, for example, the control of aerospace or maritime robotic vehicles. Recent work has brought focus onto...

Fluorescence lifetime is effective in discriminating cancerous tissue from normal tissue, but conventional discrimination methods are primarily based on statistical approaches in collaboration with prior knowledge. This paper investigates the application of deep convolutional neural networks (CNNs) for automatic differentiation of ex-vivo human lun...

3D microscopes produce real-time 3D images, but they are usually limited to low-magnification applications, such as dissection. Most compound light microscopes produce flat, 2D images because high-magnification microscope lenses have inherently shallow depth of field, rendering most of the image out of focus. A large open aperture in an optical sys...

div> Over 20,000 fluorescence lifetime images from 10 patients were collected using a fibre-based custom fluorescence lifetime imaging endomicroscopy (FLIM) system. During the data collection, various measuring conditions were applied, including exposure time, optical wavelength, and lifetime extraction approaches to obtain diverse results rich in...

div> Over 20,000 fluorescence lifetime images from 10 patients were collected using a fibre-based custom fluorescence lifetime imaging endomicroscopy (FLIM) system. During the data collection, various measuring conditions were applied, including exposure time, optical wavelength, and lifetime extraction approaches to obtain diverse results rich in...

Unlike other four year programmes, we want the integrated study portion to constantly inform and educate our students throughout their time with us. All of our students have innovative projects using cutting-edge technology to solve some of the most pressing issues in medicine today. We want our students to understand and appreciate the innovative...

Stroke is a devastating condition with profound implications for health economics and resources worldwide. Recent works showed that the use of brain-machine interfaces (BMI) could help movement improvements in severely affected chronic stroke patients. This work shows the feasibility and use of a Soft Orthotic Physiotherapy Hand Interactive Aid (SO...

Urban-planning authorities continually face the problem of optimising the allocation of green space over time in developing urban environments. To help in these decision-making processes, this thesis provides an empirical study of using evolutionary approaches to solve sequential decision making problems under uncertainty in stochastic environments...

Objective: To review the state of the art of robotic-aided hand physiotherapy for post-stroke rehabilitation, including the use of brain-machine interfaces. Each patient has a unique clinical history and, in response to personalized treatment needs, research into individualized and at-home treatment options has expanded rapidly in recent years. Th...

This work describes the design, fabrication and initial testing of a Soft Orthotic Physiotherapy Hand Interactive Aid (SOPHIA) for stroke rehabilitation. SOPHIA consists of: 1.) A soft robotic exoskeleton, 2.) A microcontroller based control system driven by a Brain-Machine Interface (BMI), and 3.) A sensorised glove for passive rehabilitation. In...

Urban-planning authorities continually face the problem of optimising the allocation of green space over time in developing urban environments. The problem is essentially a sequential decision-making task involving several interconnected and non-linear uncertainties, and requires time-intensive computation to evaluate the potential consequences of...

Evolutionary approaches are metaheuristics that can deal with the effect of noise and uncertainty in data using different strategies. In this paper is depicted the method used to cope with these elements in a dynamical location-allocation problem. The use of Monte Carlo sampling and statistical historical data that can be applied to a single and mu...

Parkinson's disease is a progressive neurodegenera-tive disorder. The biggest risk factor for developing Parkinson's disease is age and so prevalence is increasing in countries where the average age of the population is rising. Cognitive problems are common in Parkinson's disease and identifying those with the condition who are most at risk of deve...

This is an interactive demo of the first prototype of the Soft Orthotic Physiotherapy Interactive Aid project (SOPHIA). SOPHIA combines a soft exoskeleton with a brain machine interface (BMI) to create a robotic device for stroke rehabilitation, more specifically for hand motor impairment recovery. The ultimate goal of SOPHIA is to create a system...

The incidence of neurodegenerative diseases such as Parkinson’s is increasing rapidly around the world, yet the symptoms and pathology of these diseases remain incompletely understood. As a consequence, it is challenging for clinicians to provide patients with accurate diagnoses or prognoses. In this work, we use multi-objective evolutionary algori...

Parkinson's disease (PD) is a chronic neurodegenerative condition. Traditionally categorised as a movement disorder, nowadays it is recognised that PD can also lead to significant cognitive dysfunction including, in many cases, full-blown dementia. Due to the wide range of symptoms, including significant overlap with other neurodegenerative conditi...

The task of green space allocation in urban areas consists of identifying a suitable site for allocating green areas. In this proposition paper we discuss about a number of factors like crowdedness, design, distribution and size that could discourage inhabitants to visit a certain green urban area. We plan to cluster our urban residents into severa...

Urban green spaces play a crucial role in the creation of healthy environments in densely populated areas. Agent-based systems are commonly used to model processes such as green-space allocation. In some cases, this systems delegate their spatial assignation to optimisation techniques to find optimal solutions. However, the computational time compl...

Optical MEMS, Raman Spectroscopy Presentation to Edinburgh Instruments ltd Ongoing negotiations to develop a Neurological Neurosensor. . IPR Owner : Luis Acevedo

Agent-based systems are commonly used in the geographical land use sciences to model processes such as urban growth. In some cases, agents represent civic decision-makers, iteratively making decisions about the sale, purchase and development of patches of land. Based on simple assumptions, such systems are able broadly to model growth scenarios wit...

In this paper we introduce a three-step heuristic for a complex version of the Vehicle Routing Problem. The Vehicle Routing Problem is focused on the design of optimal delivery routes. A new memory-based approach is developed in order to gather highly valuable experience to predict the best routes in advance. A clustering representation is proposed...

The present abstract proposes the use of Agent-Based Complex Systems (ABCS) to model the impact of policy decisions on Climate Change. ABCS is a powerful and innovative technique of representation capable of modelling and analysing the behaviour of the key actors in climate policy as well as the consequences of their decisions.