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A multi-modal deep learning framework for enhanced breast cancer diagnosis using mammograms and clinical data. [PDF]
Sci RepIbrahim AM +6 more
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Continuous predictive mortality risk monitoring after allogeneic hematopoietic stem cell transplantation. [PDF]
Sci RepRucks N +5 more
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Personalized Type 1 Diabetes Management: Reinforcement Learning-Based Insulin Dosing and Glucose Forecasting. [PDF]
JMIR DiabetesTaku EM, Gupta V, Singhal A.
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Evaluating Explainability: A Framework for Systematic Assessment of Explainable AI Features in Medical Imaging. [PDF]
Bioengineering (Basel)Lago MA, Zamzmi G, Eich B, Delfino JG.
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Explainability in Graph Neural Networks: A Taxonomic Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022 Deep learning methods are achieving ever-increasing performance on many artificial intelligence tasks. A major limitation of deep models is that they are not amenable to interpretability.
Hao Yuan, , Shurui Gui
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2020
An aspect of User friendly AI involves explanation and better transparency of AI. Explainable AI(XAI) is an emerging area of research dedicated to explain and elucidate AI systems. In order to accomplish such an explanation, XAI uses a variety of tools, devices and frameworks.
Swaroop Panda, Shatarupa Thakurta Roy
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An aspect of User friendly AI involves explanation and better transparency of AI. Explainable AI(XAI) is an emerging area of research dedicated to explain and elucidate AI systems. In order to accomplish such an explanation, XAI uses a variety of tools, devices and frameworks.
Swaroop Panda, Shatarupa Thakurta Roy
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IEEE Transactions on Pattern Analysis and Machine Intelligence, 1993
'Explaining away' is a common pattern of reasoning in which the confirmation of one cause of an observed or believed event reduces the need to invoke alternative causes. The opposite of explaining away also an occur, where the confirmation of one cause increases belief in another. A general qualitative probabilistic analysis of intercausal reasoning is
Michael P. Wellman, Max Henrion
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'Explaining away' is a common pattern of reasoning in which the confirmation of one cause of an observed or believed event reduces the need to invoke alternative causes. The opposite of explaining away also an occur, where the confirmation of one cause increases belief in another. A general qualitative probabilistic analysis of intercausal reasoning is
Michael P. Wellman, Max Henrion
openaire +1 more source
Philosophy and TechnologyThere has been a surge of interest in explainable artificial intelligence (XAI). It is commonly claimed that explainability is necessary for trust in AI, and that this is why we need it.
Baron, S
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