Impediments to the Historical Development of the Clinical Pap Test in the United States: Their Relevance in Optimizing Cervical Cancer Screening in the State of Qatar

Three hundred surveys listing 31 questions were distributed amongst women and men at Sidra Medicine, Doha, State of Qatar, to assess knowledge and perceptions of: Cervical cancer (CxCa); preventive Pap test screening; Human Papilloma Virus (HPV) vaccination; and, need for population-based screening. Survey questions captured categorical statistical data through four categories: (1) Socio-demographic; (2) Healthcare Services; (3) Health Literacy; and, (4) Self-efficacy and Perceptions. Hypothesis 1: 12 survey questions pertaining to CxCa and Pap testing revealed 8 statistically-significant dependencies; notably, 70% of respondents were aware of CxCa; however, 31.8% were unaware that CxCa may be curable; 33.7% were unaware of the Pap test; and, 35.2% were unaware that HPV vaccination may protect against CxCa. Hypothesis 2: Two survey questions pertaining to screening practice revealed no statistically-significant dependencies; however, 67.4% of respondents strongly agreed for establishment of population-based screening in Qatar. Hypothesis 3: Three salient parallels were revealed between the Qatar and US clinical experiences: (1) Greater than 60% of symptomatic women in Qatar presented with Stage II/III CxCa in 2014, relative to 60% of women in the US with inoperable disease before 1957; (2) Estimated CxCa death rates in Qatar are 26.7%, relative to 32.2% in the US; and, (3) The burden of CxCa was under-estimated prior to emergence of death registries and epidemiologic data in the US in 1914 and 1952 respectively, relative to 2014 in Qatar. Impediments to Pap test development and application in the US stemmed from macro-dynamics (i.e., societal, economic, political situations); and, reactive micro-dynamics (i.e., professional conflicts, skepticism, conceptualization of cervical precancer). Pap test screening practices in the State of Qatar may be optimized through: (1) Tumor Registry for inclusion of precancer cases to ascertain actual CxCa incidence; (2) Organized screening with initial call to screening for asymptomatic women deemed at risk; and, (3) Reallocation of financial resources to support expanded screening for all women

Computer aided diagnosis algorithms for digital microscopy

Automatic analysis and information extraction from an image is still a highly chal- lenging research problem in the computer vision area, attempting to describe the image content with computational and mathematical techniques. Moreover the in- formation extracted from the image should be meaningful and as most discrimi- natory as possible, since it will be used to categorize its content according to the analysed problem. In the Medical Imaging domain this issue is even more felt because many important decisions that affect the patient care, depend on the use- fulness of the information extracted from the image. Manage medical image is even more complicated not only due to the importance of the problem, but also because it needs a fair amount of prior medical knowledge to be able to represent with data the visual information to which pathologist refer. Today medical decisions that impact patient care rely on the results of laboratory tests to a greater extent than ever before, due to the marked expansion in the number and complexity of offered tests. These developments promise to improve the care of patients, but the more increase the number and complexity of the tests, the more increases the possibility to misapply and misinterpret the test themselves, leading to inappropriate diagnosis and therapies. Moreover, with the increased number of tests also the amount of data to be analysed increases, forcing pathologists to devote much time to the analysis of the tests themselves rather than to patient care and the prescription of the right therapy, especially considering that most of the tests performed are just check up tests and most of the analysed samples come from healthy patients. Then, a quantitative evaluation of medical images is really essential to overcome uncertainty and subjectivity, but also to greatly reduce the amount of data and the timing for the analysis. In the last few years, many computer assisted diagno- sis systems have been developed, attempting to mimic pathologists by extracting features from the images. Image analysis involves complex algorithms to identify and characterize cells or tissues using image pattern recognition technology. This thesis addresses the main problems associated to the digital microscopy analysis in histology and haematology diagnosis, with the development of algorithms for the extraction of useful information from different digital images, but able to distinguish different biological structures in the images themselves. The proposed methods not only aim to improve the degree of accuracy of the analysis, and reducing time, if used as the only means of diagnoses, but also they can be used as intermediate tools for skimming the number of samples to be analysed directly from the pathologist, or as double check systems to verify the correct results of the automated facilities used today

Vaccination Nation: A Bioethical Feminist Inquiry into the Political, Social and Ethical Controversy Surrounding the Human Papillomavirus Vaccine

This theoretical inquiry has explored the political, social, and ethical controversy surrounding the government\u27s push to mandate the human papillomavirus (HPV) vaccine for adolescent girls. This vaccine has the potential of preventing cancer, specifically cervical cancer. There is a growing debate in this country whether this new HPV vaccine, Gardasil®, should be added to the list of school-mandated vaccines. Karen Houppert (2007) has stated that this particular vaccine protects girls and women from cervical cancer and genital warts caused by the human papillomavirus (HPV) (p. 17). So, what is the controversy? It all started with the fact that this vaccine is the first immunization produced to prevent cancer caused by a sexually transmitted disease (STD). There are several U.S. politicians that want to make the HPV vaccine a compulsory vaccine. Because an STD causes this disease there is a debate, according to Houppert, by compassionate conservatives and abstinence-only hardliners who object to mandating the vaccine since the disease was the result of a lifestyle decision (p.17). On the other hand, according to the Centers for Disease Control (CDC), Gardasil® has proven 100% effective in preventing the four strains of HPV that are responsible for most cases of cervical cancer (Manning, 2007, p. 11). So why not mandate it for adolescent girls? This question was explored further in this work using bioethical feminist theory as a theoretical framework. This study was grounded in the works of Rosemarie Tong and Susan Sherwin

Computer aided diagnosis algorithms for digital microscopy

Automatic analysis and information extraction from an image is still a highly chal- lenging research problem in the computer vision area, attempting to describe the image content with computational and mathematical techniques. Moreover the in- formation extracted from the image should be meaningful and as most discrimi- natory as possible, since it will be used to categorize its content according to the analysed problem. In the Medical Imaging domain this issue is even more felt because many important decisions that affect the patient care, depend on the use- fulness of the information extracted from the image. Manage medical image is even more complicated not only due to the importance of the problem, but also because it needs a fair amount of prior medical knowledge to be able to represent with data the visual information to which pathologist refer. Today medical decisions that impact patient care rely on the results of laboratory tests to a greater extent than ever before, due to the marked expansion in the number and complexity of offered tests. These developments promise to improve the care of patients, but the more increase the number and complexity of the tests, the more increases the possibility to misapply and misinterpret the test themselves, leading to inappropriate diagnosis and therapies. Moreover, with the increased number of tests also the amount of data to be analysed increases, forcing pathologists to devote much time to the analysis of the tests themselves rather than to patient care and the prescription of the right therapy, especially considering that most of the tests performed are just check up tests and most of the analysed samples come from healthy patients. Then, a quantitative evaluation of medical images is really essential to overcome uncertainty and subjectivity, but also to greatly reduce the amount of data and the timing for the analysis. In the last few years, many computer assisted diagno- sis systems have been developed, attempting to mimic pathologists by extracting features from the images. Image analysis involves complex algorithms to identify and characterize cells or tissues using image pattern recognition technology. This thesis addresses the main problems associated to the digital microscopy analysis in histology and haematology diagnosis, with the development of algorithms for the extraction of useful information from different digital images, but able to distinguish different biological structures in the images themselves. The proposed methods not only aim to improve the degree of accuracy of the analysis, and reducing time, if used as the only means of diagnoses, but also they can be used as intermediate tools for skimming the number of samples to be analysed directly from the pathologist, or as double check systems to verify the correct results of the automated facilities used today

Deep Learning in Medical Image Analysis

The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis

Cancer and the Politics of Care: Inequalities and interventions in global perspective

This timely volume responds to the epic impacts of cancer as a global phenomenon. Through the fine-grained lens of ethnography, the contributors present new thinking on how social, economic, race, gender and other structural inequalities intersect, compound and complicate health inequalities. Cancer experiences and impacts are explored across eleven countries: Argentina, Brazil, Denmark, France, Greece, India, Indonesia, Italy, Senegal, the United Kingdom and the United States. The volume engages with specific cancers from the point of primary prevention, to screening, diagnosis, treatment (or its absence), and end-of-life care. Cancer and the Politics of Care traverses new theoretical terrain through explicitly critiquing cancer interventions, their limitations and success, the politics that drive them, and their embeddedness in local cultures and value systems. It extends prior work on cancer, by incorporating the perspectives of patients and their families, ‘at risk’ groups and communities, health professionals, cancer advocates and educators, and patient navigators. The volume advances cross-cultural understandings of care, resisting simple dichotomies between caregiving and receiving, and reveals the fraught ethics of care that must be negotiated in resource-poor settings and stratified health systems. Its diversity and innovation ensures its wide utility among those working in and studying medical anthropology, social anthropology and other fields at the intersections of social science, medicine and health equity

Personality Identification from Social Media Using Deep Learning: A Review

Social media helps in sharing of ideas and information among people scattered around the world and thus helps in creating communities, groups, and virtual networks. Identification of personality is significant in many types of applications such as in detecting the mental state or character of a person, predicting job satisfaction, professional and personal relationship success, in recommendation systems. Personality is also an important factor to determine individual variation in thoughts, feelings, and conduct systems. According to the survey of Global social media research in 2018, approximately 3.196 billion social media users are in worldwide. The numbers are estimated to grow rapidly further with the use of mobile smart devices and advancement in technology. Support vector machine (SVM), Naive Bayes (NB), Multilayer perceptron neural network, and convolutional neural network (CNN) are some of the machine learning techniques used for personality identification in the literature review. This paper presents various studies conducted in identifying the personality of social media users with the help of machine learning approaches and the recent studies that targeted to predict the personality of online social media (OSM) users are reviewed

Texture and Colour in Image Analysis

Research in colour and texture has experienced major changes in the last few years. This book presents some recent advances in the field, specifically in the theory and applications of colour texture analysis. This volume also features benchmarks, comparative evaluations and reviews

(Re)collections: Engaging Feminist Geography with Embodied and Relational Experiences of Pregnancy Losses

With empirically-grounded and theoretically-inferred consideration in this thesis, I bring into focus a vast ‘collection’ of components entailed in lived experiences of pregnancy losses and, in particular, foreground the ways in which spaces and places are intimately involved. This includes, for example, attending to medical settings such as hospitals, workplaces, homes and gardens, online support communities, cemeteries and other memorial locations in addition to bodies which are simultaneously material and emotional. Since pregnancy losses are inter-personal, I also discuss social relations between women, their embryos, foetuses, babies and/or children, medical staff, partners, family members, friends, work colleagues, online group users and ‘wider society’. The multiplicity of components within, and across, participants’ experiences serves to simultaneously break apart and reassemble the label I selected for the research of ‘pregnancy losses’. I utilise several sub-disciplines across the thesis, finding a particularly significant and tricky tension between two particular areas I wish to engage: feminist geographies and the geographies of death and dying. My research weaves together feminist, embodied, emotional geographies through which I seek to understand experiences of pregnancy losses. In doing so, I foreground the richness, depth and complexity of lived experiences by developing understandings of pregnancy losses which embrace, rather than sanitise or marginalise, bodily materiality and social relations as well as emotional dynamics. My thesis serves to bring together and explore the recollections of pregnancy loss experiences, organised around a number of spatial contexts and activities. These are reflected in the focus of each chapter in terms of interior bodies, social relations, bodily fluids, online sites, external skins and practices of memorialisation. My discussions work to ‘collect’ together understandings about the somewhat paradoxical fullness and variety of accumulated meanings that can be held about pregnancy loss experiences

AVATAR - Machine Learning Pipeline Evaluation Using Surrogate Model

© 2020, The Author(s). The evaluation of machine learning (ML) pipelines is essential during automatic ML pipeline composition and optimisation. The previous methods such as Bayesian-based and genetic-based optimisation, which are implemented in Auto-Weka, Auto-sklearn and TPOT, evaluate pipelines by executing them. Therefore, the pipeline composition and optimisation of these methods requires a tremendous amount of time that prevents them from exploring complex pipelines to find better predictive models. To further explore this research challenge, we have conducted experiments showing that many of the generated pipelines are invalid, and it is unnecessary to execute them to find out whether they are good pipelines. To address this issue, we propose a novel method to evaluate the validity of ML pipelines using a surrogate model (AVATAR). The AVATAR enables to accelerate automatic ML pipeline composition and optimisation by quickly ignoring invalid pipelines. Our experiments show that the AVATAR is more efficient in evaluating complex pipelines in comparison with the traditional evaluation approaches requiring their execution