Mengel Michael (21 results)

Paperback. Condition: Good. Jason Pedersen; Anthony A. Black; Jeff Pina; Justin Miller; Ernie Najera; Sam Laggren; Troy Zurel; Jenn Fuguet; Christian Vilaire; Tara Abbamondi; Nathan Girten; Eric Schock; Jenn Corella; Russ Kazmierczak; Joshua Green; Nicolai Green; Matt Goodall; Madame M; JP Manzanares (illustrator). . Satisfaction 100% guaranteed.

Paperback. Condition: Very Good. Jason Pedersen; Anthony A. Black; Jeff Pina; Justin Miller; Ernie Najera; Sam Laggren; Troy Zurel; Jenn Fuguet; Christian Vilaire; Tara Abbamondi; Nathan Girten; Eric Schock; Jenn Corella; Russ Kazmierczak; Joshua Green; Nicolai Green; Matt Goodall; Madame M; JP Manzanares (illustrator). Connecting readers with great books since 1972! Used books may not include companion materials, and may have some shelf wear or limited writing. We ship orders daily and Customer Service is our top priority!

Condition: gut. 96 S. : graph. Darst. ; 21 cm, Rand mit Läsur. Sprache: Deutsch.

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: New. pp. 341.

Condition: New. pp. 341.

Condition: New. pp. 341.

Gr.-8vo. XIV, 557 S. Originaler Verlagskarton. Verlagsfrischer Zustand! (Proceedings of the Conference of the German Association of University Teachers of English, 24).

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. 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.

Hardback. Condition: New. 1. Auflage 2019.

Hardcover. Condition: New.

Hardcover. Condition: Brand New. German language. 10.31x7.52x0.79 inches. In Stock.

Paperback. Condition: New.

Hardback. Condition: New. 1. Auflage 2019.

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.