Müller Heimo (15 results)

Condition: good. All pages and cover are intact including the dust cover, if applicable . Spine may show signs of wear. Pages may include limited notes and highlighting. May include "From the library of" labels. Shrink wrap, dust covers, or boxed set case may be missing. Item may be missing bundled media.

Condition: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.

Originalbroschur. Condition: Sehr gut. 116 S. Neuwertig. - STELLA ROLLIG Video als Zumutung. Fallbeispiel (HERS): Zum Ausstellen zeitgebundener Kunst -- KLAUS VOGEL Science meets Art. Die gestalterische Sprache von Wissenschaftsausstellungen -- HELMUT LACKNER Voraussetzungen und Ergebnisse der Neueinrichtung des Technischen Museums Wien -- WOLFGANG KOS Müssen Beschriftungen auf dem Boden kleben? Über das anhaltende Desinteresse der Ausstellungsgestalter am Text-Management -- HEIMO HOFGARTNER / KATIA SCHURL "Solchen Plunder habe ich auch zu Hause, hätte nie gedacht, daß das für eine Ausstellung reicht." Reflexionen über das Ausstellungsprojekt "Berg der Erinnerungen" von Graz 2003 Kulturhauptstadt Europas. ISBN 9783851323931 Sprache: Deutsch Gewicht in Gramm: 159.

Condition: New. pp. 341.

Condition: New. pp. 341.

Condition: New. pp. 341.

Paperback. Condition: new. Paperback. Data driven Artificial Intelligence (AI) and Machine Learning (ML) in digital pathology, radiology, and dermatology is very promising. In specific cases, for example, Deep Learning (DL), even exceeding human performance. However, in the context of medicine it is important for a human expert to verify the outcome. Consequently, there is a need for transparency and re-traceability of state-of-the-art solutions to make them usable for ethical responsible medical decision support. Moreover, big data is required for training, covering a wide spectrum of a variety of human diseases in different organ systems. These data sets must meet top-quality and regulatory criteria and must be well annotated for ML at patient-, sample-, and image-level. Here biobanks play a central and future role in providing large collections of high-quality, well-annotated samples and data. The main challenges are finding biobanks containing fit-for-purpose samples, providing quality related meta-data, gaining access to standardized medical data and annotations, and mass scanning of whole slides including efficient data management solutions. The main challenges are finding biobanks containing fit-for-purpose samples, providing quality related meta-data, gaining access to standardized medical data and annotations, and mass scanning of whole slides including efficient data management solutions. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Paperback. Condition: Brand New. 356 pages. 9.25x6.10x0.94 inches. In Stock.

Taschenbuch. Condition: Neu. Artificial Intelligence and Machine Learning for Digital Pathology | State-of-the-Art and Future Challenges | Andreas Holzinger (u. a.) | Taschenbuch | Lecture Notes in Computer Science | xii | Englisch | 2020 | Springer | EAN 9783030504014 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Data driven Artificial Intelligence (AI) and Machine Learning (ML) in digital pathology, radiology, and dermatology is very promising. In specific cases, for example, Deep Learning (DL), even exceeding human performance. However, in the context of medicine it is important for a human expert to verify the outcome. Consequently, there is a need for transparency and re-traceability of state-of-the-art solutions to make them usable for ethical responsible medical decision support. Moreover, big data is required for training, covering a wide spectrum of a variety of human diseases in different organ systems. These data sets must meet top-quality and regulatory criteria and must be well annotated for ML at patient-, sample-, and image-level. Here biobanks play a central and future role in providing large collections of high-quality, well-annotated samples and data. The main challenges are finding biobanks containing ''fit-for-purpose'' samples, providing quality related meta-data, gaining access to standardized medical data and annotations, and mass scanning of whole slides including efficient data management solutions.

Paperback. Condition: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Condition: new. Questo è un articolo print on demand.

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware - Data driven Artificial Intelligence (AI) and Machine Learning (ML) in digital pathology, radiology, and dermatology is very promising. In specific cases, for example, Deep Learning (DL), even exceeding human performance. However, in the context of medicine it is important for a human expert to verify the outcome. Consequently, there is a need for transparency and re-traceability of state-of-the-art solutions to make them usable for ethical responsible medical decision support. Moreover, big data is required for training, covering a wide spectrum of a variety of human diseases in different organ systems. These data sets must meet top-quality and regulatory criteria and must be well annotated for ML at patient-, sample-, and image-level. Here biobanks play a central and future role in providing large collections of high-quality, well-annotated samples and data. The main challenges are finding biobanks containing ''fit-for-purpose'' samples, providing quality related meta-data, gaining access to standardized medical data and annotations, and mass scanning of whole slides including efficient data management solutions. 356 pp. Englisch.

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Digital pathology is a disruptive innovation that will markedly change health care in the next few years&nbspBiobanks play a central role providing large collections of high-quality well-annotated samples and data&nbspFuture.

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Data driven Artificial Intelligence (AI) and Machine Learning (ML) in digital pathology, radiology, and dermatology is very promising. In specific cases, for example, Deep Learning (DL), even exceeding human performance. However, in the context of medicine it is important for a human expert to verify the outcome. Consequently, there is a need for transparency and re-traceability of state-of-the-art solutions to make them usable for ethical responsible medical decision support.Moreover, big data is required for training, covering a wide spectrum of a variety of human diseases in different organ systems. These data sets must meet top-quality and regulatory criteria and must be well annotated for ML at patient-, sample-, and image-level. Here biobanks play a central and future role in providing large collections of high-quality, well-annotated samples and data. The main challenges are finding biobanks containing ''fit-for-purpose'' samples, providing quality related meta-data, gaining access to standardized medical data and annotations, and mass scanning of whole slides including efficient data management solutions.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 356 pp. Englisch.