A systematic review of trustworthy and explainable artificial intelligence in healthcare: Assessment of quality, bias risk, and data fusion
In the last few years, the trend in health care of embracing artificial intelligence (AI) has
dramatically changed the medical landscape. Medical centres have adopted AI applications …
dramatically changed the medical landscape. Medical centres have adopted AI applications …
Machine learning methods for small data challenges in molecular science
Small data are often used in scientific and engineering research due to the presence of
various constraints, such as time, cost, ethics, privacy, security, and technical limitations in …
various constraints, such as time, cost, ethics, privacy, security, and technical limitations in …
Survey of explainable AI techniques in healthcare
Artificial intelligence (AI) with deep learning models has been widely applied in numerous
domains, including medical imaging and healthcare tasks. In the medical field, any judgment …
domains, including medical imaging and healthcare tasks. In the medical field, any judgment …
Explainable intrusion detection for cyber defences in the internet of things: Opportunities and solutions
The field of Explainable Artificial Intelligence (XAI) has garnered considerable research
attention in recent years, aiming to provide interpretability and confidence to the inner …
attention in recent years, aiming to provide interpretability and confidence to the inner …
Graph neural networks: foundation, frontiers and applications
The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …
recent years. Graph neural networks, also known as deep learning on graphs, graph …
Interpretable and generalizable graph learning via stochastic attention mechanism
Interpretable graph learning is in need as many scientific applications depend on learning
models to collect insights from graph-structured data. Previous works mostly focused on …
models to collect insights from graph-structured data. Previous works mostly focused on …
Explainability in graph neural networks: A taxonomic survey
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 …
intelligence tasks. A major limitation of deep models is that they are not amenable to …
On explainability of graph neural networks via subgraph explorations
We consider the problem of explaining the predictions of graph neural networks (GNNs),
which otherwise are considered as black boxes. Existing methods invariably focus on …
which otherwise are considered as black boxes. Existing methods invariably focus on …
A comprehensive survey on trustworthy graph neural networks: Privacy, robustness, fairness, and explainability
Graph neural networks (GNNs) have made rapid developments in the recent years. Due to
their great ability in modeling graph-structured data, GNNs are vastly used in various …
their great ability in modeling graph-structured data, GNNs are vastly used in various …
[HTML][HTML] Towards multi-modal causability with graph neural networks enabling information fusion for explainable AI
AI is remarkably successful and outperforms human experts in certain tasks, even in
complex domains such as medicine. Humans on the other hand are experts at multi-modal …
complex domains such as medicine. Humans on the other hand are experts at multi-modal …