• case-insensitive prefix search: default
  e.g., sig matches "SIGIR" as well as "signal"
• exact word search: append dollar sign ($) to word
  e.g., graph$ matches "graph", but not "graphics"
• boolean and: separate words by space
  e.g., codd model
• boolean or: connect words by pipe symbol (|)
  e.g., graph|network

Update May 7, 2017: Please note that we had to disable the phrase search operator (.) and the boolean not operator (-) due to technical problems. For the time being, phrase search queries will yield regular prefix search result, and search terms preceded by a minus will be interpreted as regular (positive) search terms.

found 20 matches

• 2020
• 
  Pedro Furtado:
  Classification vs Deep Learning in Cancer Degree on Limited Histopathology Datasets. AI and ML for Digital Pathology 2020: 175-194
• 
  Nassima Dif, Zakaria Elberrichi:
  Deep Learning Methods for Mitosis Detection in Breast Cancer Histopathological Images: A Comprehensive Review. AI and ML for Digital Pathology 2020: 279-306
• 
  Soufiane El Hallani, Calum MacAulay, Martial Guillaud:
  Digital Image Analysis in Pathology Using DNA Stain: Contributions in Cancer Diagnostics and Development of Prognostic and Theranostic Biomarkers. AI and ML for Digital Pathology 2020: 246-263
• 
  Christiane Hartfeldt, Verena Huth, Sabrina Schmitt, Bettina Meinung, Peter Schirmacher, Michael Kiehntopf, Cornelia Specht, Michael Hummel:
  Sample Quality as Basic Prerequisite for Data Quality: A Quality Management System for Biobanks. AI and ML for Digital Pathology 2020: 89-94
• 
  Yoichi Hayashi:
  Black Box Nature of Deep Learning for Digital Pathology: Beyond Quantitative to Qualitative Algorithmic Performances. AI and ML for Digital Pathology 2020: 95-101
• 
  Michaela Kargl, Peter Regitnig, Heimo Müller, Andreas Holzinger:
  Towards a Better Understanding of the Workflows: Modeling Pathology Processes in View of Future AI Integration. AI and ML for Digital Pathology 2020: 102-117
• 
  Zisis Kozlakidis:
  Biobanks and Biobank-Based Artificial Intelligence (AI) Implementation Through an International Lens. AI and ML for Digital Pathology 2020: 195-203
• 
  Björn Lindequist, Norman Zerbe, Peter Hufnagl:
  OBDEX - Open Block Data Exchange System. AI and ML for Digital Pathology 2020: 118-135
• 
  Shane O'Sullivan, Fleur Jeanquartier, Claire Jean-Quartier, Andreas Holzinger, Dan Shiebler, Pradip Moon, Claudio Angione:
  Developments in AI and Machine Learning for Neuroimaging. AI and ML for Digital Pathology 2020: 307-320
• 
  Horst Pichler, Johann Eder:
  Supporting the Donation of Health Records to Biobanks for Medical Research. AI and ML for Digital Pathology 2020: 38-55
• 
  Milda Poceviciute, Gabriel Eilertsen, Claes Lundström:
  Survey of XAI in Digital Pathology. AI and ML for Digital Pathology 2020: 56-88
• 
  Sarni Suhaila Rahim, Vasile Palade, Ibrahim Almakky, Andreas Holzinger:
  Fuzzy Image Processing and Deep Learning for Microaneurysms Detection. AI and ML for Digital Pathology 2020: 321-339
• 
  Sarni Suhaila Rahim, Vasile Palade, Andreas Holzinger:
  Image Processing and Machine Learning Techniques for Diabetic Retinopathy Detection: A Review. AI and ML for Digital Pathology 2020: 136-154
• 
  Peter Regitnig, Heimo Müller, Andreas Holzinger:
  Expectations of Artificial Intelligence for Pathology. AI and ML for Digital Pathology 2020: 1-15
• 
  Philipp Seegerer, Alexander Binder, René Saitenmacher, Michael Bockmayr, Maximilian Alber, Philipp Jurmeister, Frederick Klauschen, Klaus-Robert Müller:
  Interpretable Deep Neural Network to Predict Estrogen Receptor Status from Haematoxylin-Eosin Images. AI and ML for Digital Pathology 2020: 16-37
• 
  Klaus Strohmenger, Christian Herta, Oliver Fischer, Jonas Annuscheit, Peter Hufnagl:
  Higher Education Teaching Material on Machine Learning in the Domain of Digital Pathology. AI and ML for Digital Pathology 2020: 155-174
• 
  Akif Burak Tosun, Filippo Pullara, Michael J. Becich, D. Lansing Taylor, S. Chakra Chennubhotla, Jeffrey L. Fine:
  HistoMapr^™: An Explainable AI (xAI) Platform for Computational Pathology Solutions. AI and ML for Digital Pathology 2020: 204-227
• 
  Norman Zerbe, Alexander Alekseychuk, Peter Hufnagl:
  Assessment and Comparison of Colour Fidelity of Whole Slide Imaging Scanners. AI and ML for Digital Pathology 2020: 264-278
• 
  Norman Zerbe, Christopher Hampf, Peter Hufnagl:
  Extension of the Identity Management System Mainzelliste to Reduce Runtimes for Patient Registration in Large Datasets. AI and ML for Digital Pathology 2020: 228-245
• 
  Andreas Holzinger, Randy Goebel, Michael Mengel, Heimo Müller:
  Artificial Intelligence and Machine Learning for Digital Pathology - State-of-the-Art and Future Challenges. Lecture Notes in Computer Science 12090, Springer 2020, ISBN 978-3-030-50401-4 [contents]