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De två viktigaste händelserna i varje man jordliv är födelse och död. Varje av dessa händelser händer en gång i en livtid. Varje individ kommer in i världen på en bestämd tid på en särskild dag. På samma sätt lämnar varje person denna värld på en särskild dag på en bestämd tid. Växelverkan av dessa två viktiga händelser definierar, till en stor grad, totalityen av den globala befolkningen på någon given tidsperiod. Huruvida det finns befolkningboom/explosionen, eller kollapsen i världen beror i sin helhet på dessa två naturliga källor. Några demographers, klassificerar emellertid flyttning (dvs. emigration och invandring eller inflyttning och ut-flyttning) som delen av källorna av befolkningtillväxt.Antecknar dessa händelser, som och, när de uppstår, inte för gyckel eller en avsluta till honom, men ganska som hjälpmedel till en avsluta. Befolkningen påverkar varje aspekt av människoliv, namely: ekonomiskt, politiskt, lagligt, socialt, kulturellt, miljö-, vård-, Etc. Det är för dessa, och annat lika viktigt resonerar att folk av vision liksom John Graunt (1620-1674); Thomas Malthus (1766-1834); Herrn James Steuart (1713-1780); William Godwin (1756-1836); och något liknande sökte, i de tidig sortdagar, att ge erkännande till befolkningen utfärdar. Detta upprättar faktumet att, även om de formella sätter in av demography, är en förhållandevis ny innovation, folk long har angå om storleksanpassa och kännetecknen av deras territoriella befolkningar för mycket en lång tid. Trots dess jättelika betydelse, verkar som om det lite tid och resurser kanaliseras in i befolkningledning i samtidaa tider.I Ghana som i många ett u-land mycket lite uppmärksamhet har givits sätta in av på varandra följande regeringar. Denna low-profile inställning in mot befolkning utfärdar, över åren, har motsatt påverkats landets samhällsekonomiska och politiska framsteg. Jag kan inte vara för fel att förutsätta att riktig utveckling har undsluppit mest afrikanska länder och framkallningsvärlden på stort främst, därför att sammanlagt av dessa länder riktig uppmärksamhet inte har varit fallen föra befolkningmaterier. Är det inte riktigt att brist av den riktiga metoden och resurser är det huvudsakligt orsakar av ledare seemingly care-free inställning in mot befolkning utfärdar i dessa ett u-land i förflutnan? Även om det kan verka, att många bevattnar, har passerat under överbrygga, och, att, saker verkar för att ha stupat apart (Chinua Achibe) där är alltid ett fönster av flykten, när alla dörrar är stängda.Informations- och kommunikationsteknologier (ICTs) är den nya utvecklingen av fönster och flyktruttar ut ur många hitherto oöverstigliga problem. Därför i detta te, skulle jag något liknande för att undersöka och undersöka hur ICTs och det berömda nya/sociala massmedia kan hjälpa att lätta problemet av non-registreringen eller den otillräckliga registreringen av livsviktiga händelser i Ghana. / THE two most important events in every man’s Earth life are birth and death. Each of these events happens once in a life time. Every individual comes into the world at a certain time on one particular day. In the same way, every person leaves this world on one particular day at a certain time. The interaction of these two important events define, to a large extent, the totality of global population at any given time period. Whether there is population boom/ explosion or collapse in the world as a whole depends on these two natural sources. Some demographers, however, classify migration (i.e. emigration and immigration or in-migration and out-migration) as part of the sources of population growth.Recording these events as and when they occur is not for fun or an end to itself, but rather as a means to an end. Population affects every aspect of human life, namely: economic, political, legal, social, cultural, environmental, health, etc. It is for these and other equally important reasons that people of vision such as John Graunt (1620-1674); Thomas Malthus (1766-1834); Sir James Steuart (1713-1780); William Godwin (1756-1836); and the like sought, in those early days, to give recognition to population issues. This establishes the fact that although the formal field of demography is a relatively recent innovation, people have long been concerned about the size and characteristics of their territorial populations for a very long time. In spite of its enormous importance, it appears that little time and resources are channeled into population management in contemporary times.In Ghana, as in many developing countries, very little attention has been given the field by successive governments. This low-profile attitude towards population issues has, over the years, adversely affected the country’s socio-economic and political progress. I may not be too wrong to postulate that true development has eluded most African countries and the developing world at large mainly because in all of these countries proper attention has not been given to population matters. Is it not true that lack of proper method and resources are the main causes of leaders’ seemingly care-free attitude towards population issues in these developing countries in the past? Even though it may seem that many waters have passed under the bridge and that, things seem to have fallen apart (Chinua Achibe) there is always a window of escape when all doors are closed.Information and Communication Technologies (ICTs) are the new generation of windows and escape routes out of many hitherto insurmountable problems. Therefore, in this thesis, I would like to explore and examine how ICTs and the famous new/social media may help alleviate the problem of non-registration or inadequate registration of vital events in Ghana.

Bridge inspection and infrastructure inspection are critical steps in the lifecycle of the built environment. Emerging technologies and data are driving factors which are disrupting the traditional processes for conducting these inspections. Because inspections are mainly conducted visually by human inspectors, this paper focuses on improving the visual inspection process with data-driven approaches. Data driven approaches, however, require significant data, which was sparse in the existing literature. Therefore, this research first examined the present state of the existing data in the research domain. We reviewed hundreds of image-based visual inspection papers which used machine learning to augment the inspection process and from this, we compiled a comprehensive catalog of over forty available datasets in the literature and identified promising, emerging techniques and trends in the field. Based on our findings in our review we contributed six significant datasets to target gaps in data in the field. The six datasets comprised of structural material segmentation, corrosion condition state segmentation, crack detection, structural detail detection, and bearing condition state classification. The contributed datasets used novel annotation guidelines and benefitted from a novel semi-automated annotation process for both object detection and pixel-level detection models. Using the data obtained from our collected sources, task-appropriate deep learning models were trained. From these datasets and models, we developed a change detection algorithm to monitor damage evolution between two inspection videos and trained a GAN-Inversion model which generated hyper-realistic synthetic bridge inspection image data and could forecast a future deterioration state of an existing bridge element. While the application of machine learning techniques in civil engineering is not wide-spread yet, this research provides impactful contribution which demonstrates the advantages that data driven sciences can provide to more economically and efficiently inspect structures, catalog deterioration, and forecast potential outcomes. / Doctor of Philosophy / Bridge inspection and infrastructure inspection are critical steps in the lifecycle of the built environment. Emerging technologies and data are driving factors which are disrupting the traditional processes for conducting these inspections. Because inspections are mainly conducted visually by human inspectors, this paper focuses on improving the visual inspection process with data-driven approaches. Data driven approaches, however, require significant data, which was sparse in the existing literature. Therefore, this research first examined the present state of the existing data in the research domain. We reviewed hundreds of image-based visual inspection papers which used machine learning to augment the inspection process and from this, we compiled a comprehensive catalog of over forty available datasets in the literature and identified promising, emerging techniques and trends in the field. Based on our findings in our review we contributed six significant datasets to target gaps in data in the field. The six datasets comprised of structural material detection, corrosion condition state identification, crack detection, structural detail detection, and bearing condition state classification. The contributed datasets used novel labeling guidelines and benefitted from a novel semi-automated labeling process for the artificial intelligence models. Using the data obtained from our collected sources, task-appropriate artificial intelligence models were trained. From these datasets and models, we developed a change detection algorithm to monitor damage evolution between two inspection videos and trained a generative model which generated hyper-realistic synthetic bridge inspection image data and could forecast a future deterioration state of an existing bridge element. While the application of machine learning techniques in civil engineering is not widespread yet, this research provides impactful contribution which demonstrates the advantages that data driven sciences can provide to more economically and efficiently inspect structures, catalog deterioration, and forecast potential outcomes.

Breast cancer continues to be a major cause of mortality among women. In recent years, machine learning has emerged as a potential tool in detecting and grading cancer. Using machine learning techniques in computational pathology has the potential to improve precision medicine, enabling more personalized and more accurate treatment plans. The machine learning models can even detect structures that cannot be seen with human eyes. The first step is often to identify tissue areas with cancerous cells using machine learning models. Those models often rely solely on Haematoxylin and Eosin slides for training due to the time-consuming and costly nature of annotations by pathologists. Because of that, valuable information for training might be lost since some cancerous cells are more visible in the immunohistochemistry slides. In this thesis, Haematoxylin and Eosin slide annotations are registered to immunohistochemistry slides for training singlestain and multi-stain models. The registration of the annotations is not straightforward since the tissue of the slides is not necessarily from consecutive cuts, and they are sometimes applied to the slide at different angles. An algorithm evaluated during the ACROBAT challenge was used to register the slides. Using the transferred annotations, individual models are trained for each stain (K167, HER2, PGR, ER). Of the single-stain model, the HER2 stain model is showing the most promising results. As a second step, a multistain model is trained using all stains. The multi-stain model performs equally well as the single-stain models specializing in individual stains. This shows that there is no need to train specialized single-stain models. Thus being able to train one model for four different stains makes it possible to detect cancer in whole slide images stained with one of those four stains without the need to train a specialized model and only needing annotations in one stain. While the multi-stain model is a nice addition this thesis shows that it is possible to reuse annotations, which reduces the amount of manual labour from pathologists and allows for training models on immunohistochemistry slides with only having annotations from one stain. / Bröstcancer fortsätter att vara en vanlig orsak till dödlighet bland kvinnor. På senare år har maskininlärning visat sig vara ett värdefullt verktyg för att upptäcka och gradera cancer. Att använda maskininlärningstekniker inom beräkningspatologi har potential att förbättra precisionsmedicinen och möjliggöra mer individanpassade och exakta behandlingsplaner. Maskininlärningsmodellerna kan till och med upptäcka strukturer som inte kan ses med mänskliga ögon. Det första steget är ofta att identifiera vävnadsområden med cancerceller med hjälp av maskininlärningsmodeller. Dessa modeller är ofta helt beroende av hematoxylin- och eosin-slidebilder för träning eftersom det är tidsödande och kostsamt för patologer att göra annoteringar. På grund av detta kan värdefull information för träning gå förlorad eftersom vissa cancerceller är mer synliga på immunohistokemiska objektglas. I den här avhandlingen registreras annoteringar från objektglas med hematoxylin och eosin på immunohistokemiska objektglas för träning av modeller med en och flera infärgningar. Registreringen av annoteringarna är inte okomplicerad eftersom vävnaden på objektglasen inte nödvändigtvis kommer från på varandra följande snitt, och de appliceras ibland på objektglaset i olika vinklar. En algoritm som utvecklades under ACROBAT-utmaningen användes för att registrera bilderna. Med hjälp av de registrerade objektglasen tränas individuella modeller för varje infärgning (K167, HER2, PGR, ER). Av modellerna för enstaka infärgningar visar modellen för HER2-infärgning de mest lovande resultaten. I ett andra steg tränas en modell med flera infärgningar med hjälp av alla infärgningar. Multi-stain-modellen presterar lika bra som single-stain-modellerna som är specialiserade på enskilda infärgningar. Detta visar att det inte finns något behov av att träna specialiserade modeller för enstaka infärgningar. Att kunna träna en modell för fyra olika färgämnen gör det alltså möjligt att upptäcka cancer i hela objektglasbilder som färgats med ett av dessa fyra färgämnen utan att behöva träna en specialiserad modell och utan att behöva göra annoteringar. Möjligheten att endast använda en modell för att förutsäga fyra olika immunohistokemiska helbilder minskade datorkostnaderna för träning och underhåll av modellen.

Magnetic Resonance Imaging (MRI) is a cornerstone in neuroimaging for studying brain anatomy and functions. Anatomical MRI images, such as T1-weighted (T1-w) scans, allow the non-invasive visualization of the brain tissues, enabling the investigation of the brain morphology and facilitating the diagnosis of both acquired (e.g., tumors, stroke lesions, infections) and congenital (e.g., malformations) brain disorders. T1-w images provide a detailed representation of brain anatomy and accurate differentiation between the main brain structures, such as white matter (WM) and gray matter (GM), therefor they are frequently used in combination with advanced sequences such as diffusion MRI (dMRI) for the computation of the structural connectivity of the brain. In particular, from the processing of dMRI data, it is possible to investigate the structures of WM through tractography techniques, obtaining a virtual representation of the WM pathways called tractogram. Since the tractogram is a collection of digital fibers representing the neuronal axons connecting the brain's cortical areas, it is the fundamental element for studying the brain's structural connectivity. A critical step for processing the tractography data is the accurate labeling of the brain tissues, usually performed through brain tissue segmentation of T1-w images. Even though the gold standard is manual segmentation, it is time-consuming and prone to intra/inter-operator variability. Automated model-based methods produce more consistent and reliable results, however, they struggle with accuracy in the case of pathological brains due to reliance on priors based on normal anatomy. Recently, deep learning (DL) has shown the potential of supervised data-driven approaches for brain tissue segmentation by leveraging the information encoded in the signal intensity of T1-w images. As a first contribution of this thesis, we reported empirical evidence that a data-driven approach is effective for brain tissue segmentation in pathological brains. By implementing a DL network trained on a large dataset of only healthy subjects, we demonstrated improvements in segmenting the brain tissues compared to models based on healthy anatomical priors, especially on severely distorted brains. Additionally, we published a benchmark for enabling an open investigation into improving tissue segmentation of distorted brains, providing a training dataset of about one thousand healthy individuals with T1-w MR images and corresponding brain tissue labels, and a test dataset includes several tens of individuals with severe brain distortions. Another crucial aspect of processing tractography data for brain connectivity analysis is the correct alignment of the WM structures across different subjects or their normalization into a common reference space, usually performed as tractography alignment. The best practice is to perform the registration using T1-w images and then apply the resulting transformation to align the tractography, despite T1-w images lacking fiber orientation information. In light of this, various methods have been proposed to leverage the information of the WM from dMRI data, ranging from scalar diffusion maps to more complex models encoding fiber orientation in the voxels. As a second contribution to the thesis, we provide a comprehensive survey of methods for conducting tractogram alignment. Additionally, we include an empirical study with the results of a quantitative comparison among the main methods for which an implementation is available. From our findings, the use of increasingly complex diffusion models does not significantly improve the alignment of tractograms. Conversely, correspondence methods that use the fibers directly to compute the alignment outperform voxel-based methods, albeit with some limitations: not producing a deformation field, operating in an unsupervised manner, and avoiding using anatomical information. Recently, geometric deep learning (GDL) models have shown promising results in handling non-grid data like tractograms, offering new possibilities for WM structure alignment. The third main contribution of this thesis is implementing a GDL model for tractogram alignment through a supervised approach guided by fiber correspondence. The alignment is predicted as the displacement of fiber points, based on a GDL registration framework that combines graph convolutional networks and differentiable loopy belief propagation, incorporating the definition of fiber structure into the encoding of the graph. Our empirical analysis demonstrates the advantages of utilizing the proposed GDL framework over traditional volumetric registration, showcasing high alignment accuracy, low inference time, and good generalization capabilities. Overall, this thesis advances the methodology for processing MRI data for brain structural connectivity, addressing the challenges of tissue segmentation and tractography alignment, proving the potential of DL approaches also in the case of pathological brains.