Classification of malignant and benign lung nodule and prediction of image label class using multi-deep model

Lung cancer has been listed as one of the world’s leading causes of death. Early diagnosis of lung nodules has great significance for the prevention of lung cancer. Despite major improvements in modern diagnosis and treatment, the five-year survival rate is only 18%. Before diagnosis, the classification of lung nodules is one important step, in particular, because automatic classification may help doctors with a valuable opinion. Although deep learning has shown improvement in the image classifications over traditional approaches, which focus on handcraft features, due to a large number of intra-class variational images and the inter-class similar images due to various imaging modalities, it remains challenging to classify lung nodule. In this paper, a multi-deep model (MD model) is proposed for lung nodule classification as well as to predict the image label class. This model is based on three phases that include multi-scale dilated convolutional blocks (MsDc), dual deep convolutional neural networks (DCNN A/B), and multi-task learning component (MTLc). Initially, the multi-scale features are derived through the MsDc process by using different dilated rates to enlarge the respective area. This technique is applied to a pair of images. Such images are accepted by dual DCNNs, and both models can learn mutually from each other in order to enhance the model accuracy. To further improve the performance of the proposed model, the output from both DCNNs split into two portions. The multi-task learning part is used to evaluate whether the input image pair is in the same group or not and also helps to classify them between benign and malignant. Furthermore, it can provide positive guidance if there is an error. Both the intra-class and inter-class (variation and similarity) of a dataset itself increase the efficiency of single DCNN. The effectiveness of mentioned technique is tested empirically by using the popular Lung Image Consortium Database (LIDC) dataset. The results show that the strategy is highly efficient in the form of sensitivity of 90.67%, specificity 90.80%, and accuracy of 90.73%

Enriching and validating geographic information on the web

The continuous growth of available data on the World Wide Web has led to an unprecedented amount of available information. However, the enormous variance in data quality and trustworthiness of information sources impairs the great potential of the large amount of vacant information. This observation especially applies to geographic information on the Web, i.e., information describing entities that are located on the Earth’s surface. With the advent of mobile devices, the impact of geographic Web information on our everyday life has substantially grown. The mobile devices have also enabled the creation of novel data sources such as OpenStreetMap (OSM), a collaborative crowd-sourced map providing open cartographic information. Today, we use geographic information in many applications, including routing, location recommendation, or geographic question answering. The processing of geographic Web information yields unique challenges. First, the descriptions of geographic entities on the Web are typically not validated. Since not all Web information sources are trustworthy, the correctness of some geographic Web entities is questionable. Second, geographic information sources on the Web are typically isolated from each other. The missing integration of information sources hinders the efficient use of geographic Web information for many applications. Third, the description of geographic entities is typically incomplete. Depending on the application, missing information is a decisive criterion for (not) using a particular data source. Due to the large scale of the Web, the manual correction of these problems is usually not feasible such that automated approaches are required. In this thesis, we tackle these challenges from three different angles. (i) Validation of geographic Web information: We validate geographic Web information by detecting vandalism in OpenStreetMap, for instance, the replacement of a street name with advertisement. To this end, we present the OVID model for automated vandalism detection in OpenStreetMap. (ii) Enrichment of geographic Web information through integration: We integrate OpenStreetMap with other geographic Web information sources, namely knowledge graphs, by identifying entries corresponding to the same world real-world entities in both data sources. We present the OSM2KG model for automated identity link discovery between OSM and knowledge graphs. (iii) Enrichment of missing information in geographic Web information: We consider semantic annotations of geographic entities on Web pages as an additional data source. We exploit existing annotations of categorical properties of Web entities as training data to enrich missing categorical properties in geographic Web information. For all of the proposed models, we conduct extensive evaluations on real-world datasets. Our experimental results confirm that the proposed solutions reliably outperform existing baselines. Furthermore, we demonstrate the utility of geographic Web Information in two application scenarios. (i) Corpus of geographic entity embeddings: We introduce the GeoVectors corpus, a linked open dataset of ready-to-use embeddings of geographic entities. With GeoVectors, we substantially lower the burden to use geographic data in machine learning applications. (ii) Application to event impact prediction: We employ several geographic Web information sources to predict the impact of public events on road traffic. To this end, we use cartographic, event, and event venue information from the Web.Durch die kontinuierliche Zunahme verfügbarer Daten im World Wide Web, besteht heute eine noch nie da gewesene Menge verfügbarer Informationen. Das große Potential dieser Daten wird jedoch durch hohe Schwankungen in der Datenqualität und in der Vertrauenswürdigkeit der Datenquellen geschmälert. Dies kann vor allem am Beispiel von geografischen Web-Informationen beobachtet werden. Geografische Web-Informationen sind Informationen über Entitäten, die über Koordinaten auf der Erdoberfläche verfügen. Die Relevanz von geografischen Web-Informationen für den Alltag ist durch die Verbreitung von internetfähigen, mobilen Endgeräten, zum Beispiel Smartphones, extrem gestiegen. Weiterhin hat die Verfügbarkeit der mobilen Endgeräte auch zur Erstellung neuartiger Datenquellen wie OpenStreetMap (OSM) geführt. OSM ist eine offene, kollaborative Webkarte, die von Freiwilligen dezentral erstellt wird. Mittlerweile ist die Nutzung geografischer Informationen die Grundlage für eine Vielzahl von Anwendungen, wie zum Beispiel Navigation, Reiseempfehlungen oder geografische Frage-Antwort-Systeme. Bei der Verarbeitung geografischer Web-Informationen müssen einzigartige Herausforderungen berücksichtigt werden. Erstens werden die Beschreibungen geografischer Web-Entitäten typischerweise nicht validiert. Da nicht alle Informationsquellen im Web vertrauenswürdig sind, ist die Korrektheit der Darstellung mancher Web-Entitäten fragwürdig. Zweitens sind Informationsquellen im Web oft voneinander isoliert. Die fehlende Integration von Informationsquellen erschwert die effektive Nutzung von geografischen Web-Information in vielen Anwendungsfällen. Drittens sind die Beschreibungen von geografischen Entitäten typischerweise unvollständig. Je nach Anwendung kann das Fehlen von bestimmten Informationen ein entscheidendes Kriterium für die Nutzung einer Datenquelle sein. Da die Größe des Webs eine manuelle Behebung dieser Probleme nicht zulässt, sind automatisierte Verfahren notwendig. In dieser Arbeit nähern wir uns diesen Herausforderungen von drei verschiedenen Richtungen. (i) Validierung von geografischen Web-Informationen: Wir validieren geografische Web-Informationen, indem wir Vandalismus in OpenStreetMap identifizieren, zum Beispiel das Ersetzen von Straßennamen mit Werbetexten. (ii) Anreicherung von geografischen Web-Information durch Integration: Wir integrieren OpenStreetMap mit anderen Informationsquellen im Web (Wissensgraphen), indem wir Einträge in beiden Informationsquellen identifizieren, die den gleichen Echtwelt-Entitäten entsprechen. (iii) Anreicherung von fehlenden geografischen Informationen: Wir nutzen semantische Annotationen von geografischen Entitäten auf Webseiten als weitere Datenquelle. Wir nutzen existierende Annotationen kategorischer Attribute von Web-Entitäten als Trainingsdaten, um fehlende kategorische Attribute in geografischen Web-Informationen zu ergänzen. Wir führen ausführliche Evaluationen für alle beschriebenen Modelle durch. Die vorgestellten Lösungsansätze erzielen verlässlich bessere Ergebnisse als existierende Ansätze. Weiterhin demonstrieren wir den Nutzen von geografischen Web-Informationen in zwei Anwendungsszenarien. (i) Korpus mit Embeddings von geografischen Entitäten: Wir stellen den GeoVectors-Korpus vor, einen verlinkten, offenen Datensatz mit direkt nutzbaren Embeddings von geografischen Web-Entitäten. Der GeoVectors-Korpus erleichtert die Nutzung von geografischen Daten in Anwendungen von maschinellen Lernen erheblich. (ii) Anwendung zur Prognose von Veranstaltungsauswirkungen: Wir nutzen Karten-, Veranstaltungs- und Veranstaltungsstätten-Daten aus dem Web, um die Auswirkungen von Veranstaltungen auf den Straßenverkehr zu prognostizieren

Imaging biomarkers extraction and classification for Prion disease

Prion diseases are a group of rare neurodegenerative conditions characterised by a high rate of progression and highly heterogeneous phenotypes. Whilst the most common form of prion disease occurs sporadically (sporadic Creutzfeldt-Jakob disease, sCJD), other forms are caused by inheritance of prion protein gene mutations or exposure to prions. To date, there are no accurate imaging biomarkers that can be used to predict the future diagnosis of a subject or to quantify the progression of symptoms over time. Besides, CJD is commonly mistaken for other forms of dementia. Due to the large heterogeneity of phenotypes of prion disease and the lack of a consistent spatial pattern of disease progression, the approaches used to study other types of neurodegenerative diseases are not satisfactory to capture the progression of the human form of prion disease. Using a tailored framework, I extracted quantitative imaging biomarkers for characterisation of patients with Prion diseases. Following the extraction of patient-specific imaging biomarkers from multiple images, I implemented a Gaussian Process approach to correlated symptoms with disease types and stages. The model was used on three different tasks: diagnosis, differential diagnosis and stratification, addressing an unmet need to automatically identify patients with or at risk of developing Prion disease. The work presented in this thesis has been extensively validated in a unique Prion disease cohort, comprising both the inherited and sporadic forms of the disease. The model has shown to be effective in the prediction of this illness. Furthermore, this approach may have used in other disorders with heterogeneous imaging features, being an added value for the understanding of neurodegenerative diseases. Lastly, given the rarity of this disease, I also addressed the issue of missing data and the limitations raised by it. Overall, this work presents progress towards modelling of Prion diseases and which computational methodologies are potentially suitable for its characterisation

Exploring variability in medical imaging

Although recent successes of deep learning and novel machine learning techniques improved the perfor- mance of classification and (anomaly) detection in computer vision problems, the application of these methods in medical imaging pipeline remains a very challenging task. One of the main reasons for this is the amount of variability that is encountered and encapsulated in human anatomy and subsequently reflected in medical images. This fundamental factor impacts most stages in modern medical imaging processing pipelines. Variability of human anatomy makes it virtually impossible to build large datasets for each disease with labels and annotation for fully supervised machine learning. An efficient way to cope with this is to try and learn only from normal samples. Such data is much easier to collect. A case study of such an automatic anomaly detection system based on normative learning is presented in this work. We present a framework for detecting fetal cardiac anomalies during ultrasound screening using generative models, which are trained only utilising normal/healthy subjects. However, despite the significant improvement in automatic abnormality detection systems, clinical routine continues to rely exclusively on the contribution of overburdened medical experts to diagnosis and localise abnormalities. Integrating human expert knowledge into the medical imaging processing pipeline entails uncertainty which is mainly correlated with inter-observer variability. From the per- spective of building an automated medical imaging system, it is still an open issue, to what extent this kind of variability and the resulting uncertainty are introduced during the training of a model and how it affects the final performance of the task. Consequently, it is very important to explore the effect of inter-observer variability both, on the reliable estimation of model’s uncertainty, as well as on the model’s performance in a specific machine learning task. A thorough investigation of this issue is presented in this work by leveraging automated estimates for machine learning model uncertainty, inter-observer variability and segmentation task performance in lung CT scan images. Finally, a presentation of an overview of the existing anomaly detection methods in medical imaging was attempted. This state-of-the-art survey includes both conventional pattern recognition methods and deep learning based methods. It is one of the first literature surveys attempted in the specific research area.Open Acces

Modeling Urban Venue Dynamics through Spatio-Temporal Metrics and Complex Networks

The ubiquity of GPS-enabled devices, mobile applications, and intelligent transportation systems have enabled opportunities to model the world at an unprecedented scale. Urban environments, in particular, have benefited from new data sources that provide granular representations of activities across space and time. As cities experienced a rise in urbanization, they also faced challenges in managing vehicle levels, congestion, and public transportation systems. Modeling these fast-paced changes through rich data from sources such as taxis, bikes, and trains has enabled prediction models capable of characterizing trends and forecasting future changes. Data-driven studies of urban mobility dynamics have been instrumental in helping deliver more contextual services to cities, support urban policy, and inform business decisions. This dissertation explores how novel algorithmic architectures and techniques reveal and predict business trends and urban development patterns. The research informing this dissertation harnesses principles from network science, modeling cities as connected networks of venues. Building upon a foundation of research in complex network theory, urban computing, and machine learning, we propose algorithms tailored for three computing tasks focused on modeling venue dynamics, characteristics, and trends. First, we predict the demand for newly opened businesses using insights from movement patterns across different regions of the city. Through this analysis we demonstrate how temporally similar areas can be successfully used as inputs to predict the visitation patterns of new venues. Next, we forecast the likelihood of business failure through a supervised learning model. We analyze the value of varying features in predicting business failure and explore their impact across new and established venues and across different cities worldwide. Finally, we present a deep learning architecture which integrates both spatial and topological features to predict the future demand for a venue. These works highlight the power of complex network measures to quantify the structure of a city and inform prediction models. This dissertation leverages vast amounts of data from spatio-temporal networks to model venue dynamics. The research puts forward evidence to support a data-driven study of geographic systems applied to fundamental questions in urban studies, retail development, and social science.Gates Cambridge Trus

Viewing Visual Analytics as Model Building

To complement the currently existing definitions and conceptual frameworks of visual analytics, which focus mainly on activities performed by analysts and types of techniques they use, we attempt to define the expected results of these activities. We argue that the main goal of doing visual analytics is to build a mental and/or formal model of a certain piece of reality reflected in data. The purpose of the model may be to understand, to forecast or to control this piece of reality. Based on this model-building perspective, we propose a detailed conceptual framework in which the visual analytics process is considered as a goal-oriented workflow producing a model as a result. We demonstrate how this framework can be used for performing an analytical survey of the visual analytics research field and identifying the directions and areas where further research is needed

Data Science in Healthcare

Data science is an interdisciplinary field that applies numerous techniques, such as machine learning, neural networks, and deep learning, to create value based on extracting knowledge and insights from available data. Advances in data science have a significant impact on healthcare. While advances in the sharing of medical information result in better and earlier diagnoses as well as more patient-tailored treatments, information management is also affected by trends such as increased patient centricity (with shared decision making), self-care (e.g., using wearables), and integrated care delivery. The delivery of health services is being revolutionized through the sharing and integration of health data across organizational boundaries. Via data science, researchers can deliver new approaches to merge, analyze, and process complex data and gain more actionable insights, understanding, and knowledge at the individual and population levels. This Special Issue focuses on how data science is used in healthcare (e.g., through predictive modeling) and on related topics, such as data sharing and data management

Advances in Public Transport Platform for the Development of Sustainability Cities

Modern societies demand high and varied mobility, which in turn requires a complex transport system adapted to social needs that guarantees the movement of people and goods in an economically efficient and safe way, but all are subject to a new environmental rationality and the new logic of the paradigm of sustainability. From this perspective, an efficient and flexible transport system that provides intelligent and sustainable mobility patterns is essential to our economy and our quality of life. The current transport system poses growing and significant challenges for the environment, human health, and sustainability, while current mobility schemes have focused much more on the private vehicle that has conditioned both the lifestyles of citizens and cities, as well as urban and territorial sustainability. Transport has a very considerable weight in the framework of sustainable development due to environmental pressures, associated social and economic effects, and interrelations with other sectors. The continuous growth that this sector has experienced over the last few years and its foreseeable increase, even considering the change in trends due to the current situation of generalized crisis, make the challenge of sustainable transport a strategic priority at local, national, European, and global levels. This Special Issue will pay attention to all those research approaches focused on the relationship between evolution in the area of transport with a high incidence in the environment from the perspective of efficiency