Ralf Huss

Michael Grunkin

Artificial Intelligence Applications in Human Pathology deals with the latest topics in biomedical research and clinical cancer diagnostics. With chapters provided by true international experts in the field, this book gives real examples of the implementation of AI and machine learning in human pathology.Advances in machine learning and AI in general have propelled computational and general pathology research. Today, computer systems approach the diagnostic levels achieved by humans for certain well-defined tasks in pathology. At the same time, pathologists are faced with an increased workload both quantitatively (numbers of cases) and qualitatively (the amount of work per case, with increasing treatment options and the type of data delivered by pathologists also expected to become more fine-grained). AI will support and leverage mathematical tools and implement data-driven methods as a center for data interpretation in modern tissue diagnosis and pathology. Digital or computational pathology will also foster the training of future computational pathologists, those with both pathology and non-pathology backgrounds, who will eventually decide that AI-based pathology will serve as an indispensable hub for data-related research in a global health care system.Some of the specific topics explored within include an introduction to DL as applied to Pathology, Standardized Tissue Sampling for Automated Analysis, integrating Computational Pathology into Histopathology workflows. Readers will also find examples of specific techniques applied to specific diseases that will aid their research and treatments including but not limited to; Tissue Cartography for Colorectal Cancer, Ki-67 Measurements in Breast Cancer, and Light-Sheet Microscopy as applied to Virtual Histology.The key role for pathologists in tissue diagnostics will prevail and even expand through interdisciplinary work and the intuitive use of an advanced and interoperating (AI-supported) pathology workflow delivering novel and complex features that will serve the understanding of individual diseases and of course the patient.

Artificial Intelligence Applications In Human Pathology

Die Informationstechnologien haben, wie in alle Lebensbereiche, längst auch Einzug in Medizin und Gesundheitsversorgung gehalten: - digitale Systeme erstellen Diagnosen, errechnen Krankheitsrisiken und geben individuelle Therapieempfehlungen, - neue Technologien eröffnen ungeahnte Behandlungsmöglichkeiten, - aus der Analyse großer Datenmengen ergeben sich ein tieferes Verständnis der Entstehung und der Verläufe von Krankheiten und neue Therapieideen. In diesem Spannungsfeld verändern sich auch die Erwartungen, die Patient und Arzt aneinander stellen, und ihr jeweiliges Rollenverständnis. In sieben anschaulich und einprägsam geschriebenen Kapiteln stellt der Autor die Hauptfelder der Digitalisierung in der Medizin dar, sowohl die Chancen, die sie für viele Patienten beinhalten, als auch die Gefahren, die sie für die Bewahrung der Privatsphäre und die am Patienten orientierte ärztliche Behandlung mit sich bringen können – eine gut lesbare Einführung für alle, die sich für die Entwicklung der Medizin und des Gesundheitswesens interessieren.

Künstliche Intelligenz, Robotik und Big Data in der Medizin

Gerd Binnig

Günter Schmidt

In the age of digitization our society is transformed into a new state. In particular, machine intelligence dramatically elevates our capability to create and digest information. Naturally, healthcare is also impacted by this trend and will even be more transformed into a informatic driven discipline in the future. In the most important area of histo-pathology, the interpretation of tissue slices from cancer patients, informatics will have an early and huge impact on treatment decisions and probably will act as the leading discipline for this transformation in medicine. Tissue Phenomics provides a comprehensive methodology aiming at the discovery of the most accurate tissue-based decision support algorithm by close integration of assay development, image analysis and bioinformatics, and optimization feedback loops. In this book the methodology of Tissue Phenomics and its benefits and wealth’s are described. The different components of Tissue Phenomics are explained in the different chapters. In the chapters 2 to 4 of this book different authors describe various approaches on how to convert the wealth of tissue slide pixel data into mineable information using knowledge-based and data-driven image analysis methods. Subsequently, the datafication of images and the bioinformatics part plays a crucial role in generating prognostic and predictive models for disease progression. The integration of other data sources such as genomics, radiomics and patient related information is also important and is described as well. As discussed in chapters 5 and 6, these models may classify patients in distinct groups such as those responding to a given therapy. Since Tissue Phenomics provides a huge set of potentially prognostic features (phenes), one focus of both chapters is robust feature selection methods by advanced Monte-Carlo cross validation algorithms. In chapter 7 we discuss multiple application examples of Tissue Phenomics in academic and commercial settings and its tremendous impact to advances in biomedical sciences. Building on the successes in research, chapters 8 and 9 discuss applications in clinical environments and provide a flavor to the future envision in chapter 10, where tissue datafication and subsequent patient profiling is part of every routine examination, with the goal to best match patients with the most successful therapy, as predicted by tissue phenes.

Ralf Huss

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