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AI/ML Digital Pathology 2020 : AI/Machine Learning for Digital Pathology | |||||||||||||
| Link: https://human-centered.ai/springer-lncs-ai-machine-learning-for-digital-pathology/ | |||||||||||||
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Call For Papers | |||||||||||||
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Papers are sought cross-domain from Artificial Intelligence/Machine Learning for Digital Pathology, including but not limited to (list not yet complete, in no order):
Weakly supervised learning Multiple Instance Learning Multi-Classifier Systems Transparent Machine Learning Interpretable Machine Learning Explainable AI (exAI) Explainability and Causability Ethical, Social and Legal Aspects of AI Transfer Learning Privacy-Preserving Machine Learning Performance measures Conditional random fields Deep Learning approaches Ontologies and Machine Learning Interactive Machine Learning Identification of diagnostic, prognostic and theragnostic biomarkers Data preprocessing, Data mapping, Data fusion, Data integration, Data mapping Data provenance and data curation Biobank-sample and data quality |
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