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020 _a9783030504021
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024 7 _a10.1007/978-3-030-50402-1
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
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072 7 _aUYQ
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082 0 4 _a006.3
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245 1 0 _aArtificial Intelligence and Machine Learning for Digital Pathology
_h[electronic resource] :
_bState-of-the-Art and Future Challenges /
_cedited by Andreas Holzinger, Randy Goebel, Michael Mengel, Heimo Müller.
250 _a1st ed. 2020.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2020.
300 _aXII, 341 p. 95 illus., 84 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aLecture Notes in Artificial Intelligence,
_x2945-9141 ;
_v12090
505 0 _aExpectations 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.
520 _aData 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.
650 0 _aArtificial intelligence.
_93407
650 0 _aComputers.
_98172
650 0 _aDatabase management.
_93157
650 0 _aSocial sciences
_xData processing.
_983360
650 0 _aData protection.
_97245
650 0 _aComputer vision.
_9111688
650 1 4 _aArtificial Intelligence.
_93407
650 2 4 _aComputing Milieux.
_955441
650 2 4 _aDatabase Management.
_93157
650 2 4 _aComputer Application in Social and Behavioral Sciences.
_931815
650 2 4 _aData and Information Security.
_931990
650 2 4 _aComputer Vision.
_9111689
700 1 _aHolzinger, Andreas.
_eeditor.
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_4http://id.loc.gov/vocabulary/relators/edt
_9111690
700 1 _aGoebel, Randy.
_eeditor.
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_4http://id.loc.gov/vocabulary/relators/edt
_9111691
700 1 _aMengel, Michael.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_9111692
700 1 _aMüller, Heimo.
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_9111693
710 2 _aSpringerLink (Online service)
_9111694
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
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776 0 8 _iPrinted edition:
_z9783030504038
830 0 _aLecture Notes in Artificial Intelligence,
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_v12090
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856 4 0 _uhttps://doi.org/10.1007/978-3-030-50402-1
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