Representación de imágenes de histopatología utilizada en tareas de análisis automático: estado del arte

This paper presents a review of the state-of-the-art in histopathology image representation used in automatic image analysis tasks. Automatic analysis of histopathology images is important for building computer-assisted diagnosis tools, automatic image enhancing systems and virtual microscopy systems, among other applications. Histopathology images have a rich mix of visual patterns with particularities that make them difficult to analyze. The paper discusses these particularities, the acquisition process and the challenges found when doing automatic analysis. Second an overview of recent works and methods addressed to deal with visual content representation in different automatic image analysis tasks is presented. Third an overview of applications of image representation methods in several medical domains and tasks is presented. Finally, the paper concludes with current trends of automatic analysis of histopathology images like digital pathology

The Artificial Intelligence in Digital Pathology and Digital Radiology: Where Are We?

This book is a reprint of the Special Issue entitled "The Artificial Intelligence in Digital Pathology and Digital Radiology: Where Are We?". Artificial intelligence is extending into the world of both digital radiology and digital pathology, and involves many scholars in the areas of biomedicine, technology, and bioethics. There is a particular need for scholars to focus on both the innovations in this field and the problems hampering integration into a robust and effective process in stable health care models in the health domain. Many professionals involved in these fields of digital health were encouraged to contribute with their experiences. This book contains contributions from various experts across different fields. Aspects of the integration in the health domain have been faced. Particular space was dedicated to overviewing the challenges, opportunities, and problems in both radiology and pathology. Clinal deepens are available in cardiology, the hystopathology of breast cancer, and colonoscopy. Dedicated studies were based on surveys which investigated students and insiders, opinions, attitudes, and self-perception on the integration of artificial intelligence in this field

Representation learning for histopathology image analysis

Abstract. Nowadays, automatic methods for image representation and analysis have been successfully applied in several medical imaging problems leading to the emergence of novel research areas like digital pathology and bioimage informatics. The main challenge of these methods is to deal with the high visual variability of biological structures present in the images, which increases the semantic gap between their visual appearance and their high level meaning. Particularly, the visual variability in histopathology images is also related to the noise added by acquisition stages such as magnification, sectioning and staining, among others. Many efforts have focused on the careful selection of the image representations to capture such variability. This approach requires expert knowledge as well as hand-engineered design to build good feature detectors that represent the relevant visual information. Current approaches in classical computer vision tasks have replaced such design by the inclusion of the image representation as a new learning stage called representation learning. This paradigm has outperformed the state-of-the-art results in many pattern recognition tasks like speech recognition, object detection, and image scene classification. The aim of this research was to explore and define a learning-based histopathology image representation strategy with interpretative capabilities. The main contribution was a novel approach to learn the image representation for cancer detection. The proposed approach learns the representation directly from a Basal-cell carcinoma image collection in an unsupervised way and was extended to extract more complex features from low-level representations. Additionally, this research proposed the digital staining module, a complementary interpretability stage to support diagnosis through a visual identification of discriminant and semantic features. Experimental results showed a performance of 92% in F-Score, improving the state-of-the-art representation by 7%. This research concluded that representation learning improves the feature detectors generalization as well as the performance for the basal cell carcinoma detection task. As additional contributions, a bag of features image representation was extended and evaluated for Alzheimer detection, obtaining 95% in terms of equal error classification rate. Also, a novel perspective to learn morphometric measures in cervical cells based on bag of features was presented and evaluated obtaining promising results to predict nuclei and cytoplasm areas.Los métodos automáticos para la representación y análisis de imágenes se han aplicado con éxito en varios problemas de imagen médica que conducen a la aparición de nuevas áreas de investigación como la patología digital. El principal desafío de estos métodos es hacer frente a la alta variabilidad visual de las estructuras biológicas presentes en las imágenes, lo que aumenta el vacío semántico entre su apariencia visual y su significado de alto nivel. Particularmente, la variabilidad visual en imágenes de histopatología también está relacionada con el ruido añadido por etapas de adquisición tales como magnificación, corte y tinción entre otros. Muchos esfuerzos se han centrado en la selección de la representacion de las imágenes para capturar dicha variabilidad. Este enfoque requiere el conocimiento de expertos y el diseño de ingeniería para construir buenos detectores de características que representen la información visual relevante. Los enfoques actuales en tareas de visión por computador han reemplazado ese diseño por la inclusión de la representación en la etapa de aprendizaje. Este paradigma ha superado los resultados del estado del arte en muchas de las tareas de reconocimiento de patrones tales como el reconocimiento de voz, la detección de objetos y la clasificación de imágenes. El objetivo de esta investigación es explorar y definir una estrategia basada en el aprendizaje de la representación para imágenes histopatológicas con capacidades interpretativas. La contribución principal de este trabajo es un enfoque novedoso para aprender la representación de la imagen para la detección de cáncer. El enfoque propuesto aprende la representación directamente de una colección de imágenes de carcinoma basocelular en forma no supervisada que permite extraer características más complejas a partir de las representaciones de bajo nivel. También se propone el módulo de tinción digital, una nueva etapa de interpretabilidad para apoyar el diagnóstico a través de una identificación visual de las funciones discriminantes y semánticas. Los resultados experimentales mostraron un rendimiento del 92% en términos de F-Score, mejorando la representación del estado del arte en un 7%. Esta investigación concluye que el aprendizaje de la representación mejora la generalización de los detectores de características así como el desempeño en la detección de carcinoma basocelular. Como contribuciones adicionales, una representación de bolsa de caracteristicas (BdC) fue ampliado y evaluado para la detección de la enfermedad de Alzheimer, obteniendo un 95% en términos de EER. Además, una nueva perspectiva para aprender medidas morfométricas en las células del cuello uterino basado en BdC fue presentada y evaluada obteniendo resultados prometedores para predecir las areás del nucleo y el citoplasma.Maestrí

Essentially yours: the protection of human genetic information in Australia

ALRC Report 96 (tabled May 2003)  was the product of a two-year inquiry by the ALRC and the Australian Health Ethics Committee (AHEC) of the NHMRC, involving extensive research and widespread public consultation.The inquiry was the most comprehensive ever undertaken into these issues in Australia or overseas. The report covers an extensive range of activities in which genetic information plays—or soon will play—an important role. The two-volume, 1200 page report makes 144 recommendations about how Australia should deal with the ethical, legal and social implications of the New Genetics. This Report reflects the law as at 14 March 2003

Clinical foundations and information architecture for the implementation of a federated health record service

Clinical care increasingly requires healthcare professionals to access patient record information that may be distributed across multiple sites, held in a variety of paper and electronic formats, and represented as mixtures of narrative, structured, coded and multi-media entries. A longitudinal person-centred electronic health record (EHR) is a much-anticipated solution to this problem, but its realisation is proving to be a long and complex journey. This Thesis explores the history and evolution of clinical information systems, and establishes a set of clinical and ethico-legal requirements for a generic EHR server. A federation approach (FHR) to harmonising distributed heterogeneous electronic clinical databases is advocated as the basis for meeting these requirements. A set of information models and middleware services, needed to implement a Federated Health Record server, are then described, thereby supporting access by clinical applications to a distributed set of feeder systems holding patient record information. The overall information architecture thus defined provides a generic means of combining such feeder system data to create a virtual electronic health record. Active collaboration in a wide range of clinical contexts, across the whole of Europe, has been central to the evolution of the approach taken. A federated health record server based on this architecture has been implemented by the author and colleagues and deployed in a live clinical environment in the Department of Cardiovascular Medicine at the Whittington Hospital in North London. This implementation experience has fed back into the conceptual development of the approach and has provided "proof-of-concept" verification of its completeness and practical utility. This research has benefited from collaboration with a wide range of healthcare sites, informatics organisations and industry across Europe though several EU Health Telematics projects: GEHR, Synapses, EHCR-SupA, SynEx, Medicate and 6WINIT. The information models published here have been placed in the public domain and have substantially contributed to two generations of CEN health informatics standards, including CEN TC/251 ENV 13606

New perspectives of genetic disorders in cattle

In the last decades a negative trend in inbreeding has accompanied the evident improvement in productivity and performance of bovine domestic population, predisposing to the occurrence of recessively inherited disorders. The objectives of this thesis were: a) the study of genetic diseases applying a “forward genetic approach” (FGA); b) the estimation of the prevalence of deleterious alleles responsible for eight recessive disorders in different breeds; c) the collection of well-characterized materials in a Biobank for Bovine Genetic Disorders. The FGA allowed the identification of seven new recessive deleterious variants (Paunch calf syndrome - KDM2B; Congenital cholesterol deficiency - APOB; Ichthyosis congenita - FA2H; Hypotrichosis - KRT71; Hypotrichosis - HEPHL1; Achromatopsia - CNGB3; Hemifacial microsomia – LAMB1) and of seven new de novo dominant deleterious variants (Achondrogenesis type II - two variants in COL2A1; Osteogenesis imperfecta - COL1A1; Skeletal-cardio-enteric dysplasia - MAP2K2; Congenital neuromuscular channelopathy - KGNG1; Epidermolysis bullosa simplex - KRT5; Classical Ehlers-Danlos syndrome - COL5A2) in different breeds, associated with a large spectrum of phenotypes affecting different systems. The FGA was based on the sequence of a clinical, genealogical, gross- and/or histopathological and genomic study. In particular, a WGS trio-approach (patient, dam and sire) was applied. The prevalence of deleterious alleles was calculated for the Pseudomyotonia congenita, Paunch calf syndrome, Hemifacial microsomia, Congenital bilateral cataract, Ichthyosis congenita, Ichthyosis fetalis, Achromatopsia and Hypotrichosis. A particular concern resulted the allelic frequency of 12% for the Paunch calf syndrome in Romagnola cattle. In respect to the Biobank for Bovine Genetic Diseases, biological materials of clinical cases and their available relatives as well as controls used for the allelic frequency estimations were stored at -20 °C. Altogether, around 16.000 samples were added to the biobank

Gratitude in Healthcare: an interdisciplinary inquiry

The expression and reception of gratitude is a significant dimension of interpersonal communication in care-giving relationships. Although there is a growing body of evidence that practising gratitude has health and wellbeing benefits for the giver and receiver, gratitude as a social emotion made in interaction has received comparatively little research attention. To address this gap, this thesis draws on a portfolio of qualitative methods to explore the ways in which gratitude is constituted in care provision in personal, professional, and public discourse. This research is informed by a discursive psychology approach in which gratitude is analysed, not as a morally virtuous character trait, but as a purposeful, performative social action that is mutually co-constructed in interaction.I investigate gratitude through studies that approach it on a meta, meso, macro, and micro level. Key intellectual traditions that underpin research literature on gratitude in healthcare are explored through a metanarrative review. Six underlying metanarratives were identified: social capital; gifts; care ethics; benefits of gratitude; staff wellbeing; and gratitude as an indicator of quality of care. At the meso (institutional) level, a narrative analysis of an archive of letters between patients treated for tuberculosis and hospital almoners positions gratitude as participating in a Maussian gift-exchange ritual in which communal ties are created and consolidated.At the macro (societal) level, a discursive analysis of tweets of gratitude to the National Health Service at the outset of the Covid-19 pandemic shows that attitudes to gratitude were dynamic in response to events, with growing unease about deflecting attention from risk reduction for those working in the health and social care sectors. A follow-up analysis of the clap-for-carers movement implicates gratitude in embodied, symbolic, and imagined performances in debates about care justice. At the micro (interpersonal) level, an analysis of gratitude encounters broadcast in the BBC documentary series, Hospital, uses pragmatics and conversation analysis to argue that gratitude is an emotion made in talk, with the uptake of gratitude opportunities influencing the course of conversational sequencing. The findings challenge the oftenmade distinction between task-oriented and relational conversation in healthcare.Moral economics are paradigmatic in the philosophical conceptualisation of gratitude. My research shows that, although balance-sheet reciprocity characterised the institutional culture of the voluntary hospital, it is hardly ever a feature ofinterpersonal gratitude encounters. Instead, gratitude is accomplished as shared moments of humanity through negotiated encounters infused with affect. Gratitude should never be instrumentalised as compensating for unsafe, inadequatelyrenumerated work. Neither should its potential to enhance healthcare encounters be underestimated. Attention to gratitude can participate in culture change by affirming modes of acting, emoting, relating, expressing, and connecting that intersect with care justice.This thesis speaks to gratitude as a culturally salient indicator of what people express as worthy of appreciation. It calls for these expressions to be more closely attended to, not only as useful feedback that can inform change, but also because gratitude is a resource on which we can draw to enhance and enrich healthcare as a communal, collaborative, cooperative endeavour