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E-grāmata: Artificial Intelligence Applications In Human Pathology

Edited by (Visiopharm, Denmark), Edited by (Univ Hospital Augsburg, Germany)
  • Formāts: 336 pages
  • Izdošanas datums: 04-Mar-2022
  • Izdevniecība: World Scientific Europe Ltd
  • Valoda: eng
  • ISBN-13: 9781800611405
  • Formāts - PDF+DRM
  • Cena: 84,53 €*
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  • Formāts: 336 pages
  • Izdošanas datums: 04-Mar-2022
  • Izdevniecība: World Scientific Europe Ltd
  • Valoda: eng
  • ISBN-13: 9781800611405

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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.

Foreword v
About the Editors vii
Chapter 1 Introduction: Integration of Computational Pathology and AI Application into Histopathology Workflow
1(8)
Ralf Huss
Chapter 2 Standardized Tissue Sampling for Automated Analysis and Global Trial Success
9(12)
Eike von Leitner
Philipp Layer
Hartmut Juhl
Chapter 3 Light-Sheet Microscopy as a Novel Tool for Virtual Histology
21(12)
Rene Hagerling
Fabian Mohr
Chapter 4 Stain Quality Management and Biomarker Analysis
33(38)
Soren Nielsen
Chapter 5 Measuring Ki-67 in Breast Cancer: Past, Present, and Future
71(28)
Andrew Dodson
Chapter 6 Multiplex: From Acquisition to Analysis
99(38)
Regan Baird
David Mason
Chapter 7 An Introduction to Deep Learning in Pathology
137(16)
Jeremias Krause
Heike I. Grabsch
Jakob Nikolas Kather
Chapter 8 AI-Driven Precision Pathology: Challenges and Innovations in Tissue Biomarker Analysis for Diagnosis
153(50)
Dirk Vossen
Jeppe Thagaard
Fabian Schneider
Rasmus Norre Sorensen
Johan Dore
Esther Abels
Amanda Lowe
Mogens Vyberg
Michael Grunkin
Chapter 9 Tissue Cartography for Colorectal Cancer
203(40)
Volker Bruns
Michaela Benz
Carol Geppert
Chapter 10 Graph Representation Learning and Explainability in Breast Cancer Pathology: Bridging the Gap between AI and Pathology Practice
243(44)
Pushpak Pati
Guillaume Jaume
Antonio Foncubierta-Rodriguez
Florinda Feroce
Giosue Scognamiglio
Anna Maria Anniciello
Nadia Brancati
Maria Frucci
Daniel Riccio
Jean-Philippe Thiran
Orcun Goksel
Maria Gabrani
Chapter 11 AI-Driven Design of Disease Sensors: Theoretical Foundations
287(30)
Simone Bianco
Sara Capponi
Shangying Wang
Index 317