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Artificial Intelligence and Machine Learning for Digital Pathology

State-of-the-Art and Future Challenges
BuchKartoniert, Paperback
341 Seiten
Englisch
Springererschienen am21.06.20201st ed. 2020
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.mehr
Verfügbare Formate
BuchKartoniert, Paperback
EUR90,94
E-BookPDF1 - PDF WatermarkE-Book
EUR90,94

Produkt

KlappentextThe 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.
Details
ISBN/GTIN978-3-030-50401-4
ProduktartBuch
EinbandartKartoniert, Paperback
Verlag
Erscheinungsjahr2020
Erscheinungsdatum21.06.2020
Auflage1st ed. 2020
ReiheLecture Notes in Artificial Intelligence
Seiten341 Seiten
SpracheEnglisch
Gewicht540 g
IllustrationenXII, 341 p. 95 illus., 84 illus. in color.
Artikel-Nr.48467163

Inhalt/Kritik

Inhaltsverzeichnis
Expectations of Artificial Intelligence for Pathology.- Interpretable Deep Neural Network to Predict Estrogen Receptor Status from Haematoxylin-Eosin Images.- Supporting the Donation of Health Records to Biobanks for Medical Research.- Survey of XAI in Digital Pathology.- Sample Quality as Basic Prerequisite for Data Quality: A Quality Management System for Biobanks.- Black Box Nature of Deep Learning for Digital Pathology: Beyond Quantitative to Qualitative Algorithmic Performances.- Towards a Better Understanding of the Workflows: Modeling Pathology Processes in View of Future AI Integration.- OBDEX - Open Block Data Exchange System.- Image Processing and Machine Learning Techniques for Diabetic Retinopathy Detection: A Review.- Higher Education Teaching Material on Machine Learning in the Domain of Digital Pathology.- Classification vs Deep Learning in Cancer Degree on Limited Histopathology Datasets.- Biobanks and Biobank-Based Artificial Intelligence (AI) Implementation Throughan International Lens.- HistoMapr: An Explainable AI (xAI) Platform for Computational Pathology Solutions.- Extension of the Identity Management System Mainzelliste to Reduce Runtimes for Patient Registration in Large Datasets.- Digital Image Analysis in Pathology Using DNA Stain: Contributions in Cancer Diagnostics and Development of Prognostic and Theranostic Biomarkers.- Assessment and Comparison of Colour Fidelity of Whole slide imaging scanners.- Deep Learning Methods for Mitosis Detection in Breast Cancer Histopathological Images: a Comprehensive Review.- Developments in AI and Machine Learning for Neuroimaging.mehr

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