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Lecture Notes in Artificial Intelligence Lect.Notes ComputerState-of-the-Art Surveys

Artificial Intelligence and Machine Learning for Digital Pathology

State-of-the-Art and Future Challenges

Editors: Holzinger, A., Goebel, R., Mengel, M., Müller, H. (Eds.)

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  • Digital pathology is a disruptive innovation that will markedly change health care in the next few years
  •  
  • Biobanks play a central role providing large collections of high-quality well-annotated samples and data
  •  
  • Future broad applications of Artificial Intelligence in digital pathology
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About this book

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.


Table of contents (19 chapters)

Table of contents (19 chapters)
  • Expectations of Artificial Intelligence for Pathology

    Pages 1-15

    Regitnig, Peter (et al.)

  • Interpretable Deep Neural Network to Predict Estrogen Receptor Status from Haematoxylin-Eosin Images

    Pages 16-37

    Seegerer, Philipp (et al.)

  • Supporting the Donation of Health Records to Biobanks for Medical Research

    Pages 38-55

    Pichler, Horst (et al.)

  • Survey of XAI in Digital Pathology

    Pages 56-88

    Pocevičiūtė, Milda (et al.)

  • Sample Quality as Basic Prerequisite for Data Quality: A Quality Management System for Biobanks

    Pages 89-94

    Hartfeldt, Christiane (et al.)

Buy this book

eBook $59.99
price for USA in USD
  • ISBN 978-3-030-50402-1
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover $79.99
price for USA in USD
  • ISBN 978-3-030-50401-4
  • Free shipping for individuals worldwide
  • Immediate ebook access, if available*, with your print order
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
Artificial Intelligence and Machine Learning for Digital Pathology
Book Subtitle
State-of-the-Art and Future Challenges
Editors
  • Andreas Holzinger
  • Randy Goebel
  • Michael Mengel
  • Heimo Müller
Series Title
Lecture Notes in Artificial Intelligence
Series Volume
12090
Copyright
2020
Publisher
Springer International Publishing
Copyright Holder
Springer Nature Switzerland AG
eBook ISBN
978-3-030-50402-1
DOI
10.1007/978-3-030-50402-1
Softcover ISBN
978-3-030-50401-4
Edition Number
1
Number of Pages
XII, 341
Number of Illustrations
11 b/w illustrations, 84 illustrations in colour
Topics

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