Artificial Intelligence and Machine Learning for Digital Pathology

State-of-the-Art and Future Challenges

  • Andreas Holzinger
  • Randy Goebel
  • Michael Mengel
  • Heimo Müller

Part of the Lecture Notes in Computer Science book series (LNCS, volume 12090)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 12090)

Table of contents

  1. Front Matter
    Pages i-xii
  2. Peter Regitnig, Heimo Müller, Andreas Holzinger
    Pages 1-15
  3. Philipp Seegerer, Alexander Binder, René Saitenmacher, Michael Bockmayr, Maximilian Alber, Philipp Jurmeister et al.
    Pages 16-37
  4. Milda Pocevičiūtė, Gabriel Eilertsen, Claes Lundström
    Pages 56-88
  5. Christiane Hartfeldt, Verena Huth, Sabrina Schmitt, Bettina Meinung, Peter Schirmacher, Michael Kiehntopf et al.
    Pages 89-94
  6. Michaela Kargl, Peter Regitnig, Heimo Müller, Andreas Holzinger
    Pages 102-117
  7. Björn Lindequist, Norman Zerbe, Peter Hufnagl
    Pages 118-135
  8. Sarni Suhaila Rahim, Vasile Palade, Andreas Holzinger
    Pages 136-154
  9. Klaus Strohmenger, Christian Herta, Oliver Fischer, Jonas Annuscheit, Peter Hufnagl
    Pages 155-174
  10. Akif Burak Tosun, Filippo Pullara, Michael J. Becich, D. Lansing Taylor, S. Chakra Chennubhotla, Jeffrey L. Fine
    Pages 204-227
  11. Norman Zerbe, Alexander Alekseychuk, Peter Hufnagl
    Pages 264-278
  12. Shane O’Sullivan, Fleur Jeanquartier, Claire Jean-Quartier, Andreas Holzinger, Dan Shiebler, Pradip Moon et al.
    Pages 307-320
  13. Sarni Suhaila Rahim, Vasile Palade, Ibrahim Almakky, Andreas Holzinger
    Pages 321-339
  14. Back Matter
    Pages 341-341

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.


artificial intelligence bioinformatics computer science computer systems computer vision databases deep learning education engineering expert systems image analysis image processing information systems information technology intelligent systems learning machine learning mathematics medical images neural networks

Editors and affiliations

  1. 1.Medical University of GrazGrazAustria
  2. 2.University of AlbertaEdmontonCanada
  3. 3.University of AlbertaEdmontonCanada
  4. 4.Medical University of GrazGrazAustria

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