The deployment of automated deep-learning classifiers in clinical practice has the potential to streamline the diagnosis process and improve the diagnosis accuracy, but the acceptance of those classifiers relies on both their accuracy and interpretab...
Deep neural networks typically require accurate and a large number of annotations to achieve outstanding performance in medical image segmentation. One-shot and weakly-supervised learning are promising research directions that reduce labeling effort ...
Automated volumetric meshing of patient-specific heart geometry can help expedite various biomechanics studies, such as post-intervention stress estimation. Prior meshing techniques often neglect important modeling characteristics for successful down...
Open set recognition (OSR) aims to accurately classify known diseases and recognize unseen diseases as the unknown class in medical scenarios. However, in existing OSR approaches, gathering data from distributed sites to construct large-scale central...
Participatory data stewardship (PDS) empowers individuals to shape and govern their data via responsible collection and use. As artificial intelligence (AI) requires massive amounts of data, research must assess what factors predict consumers' willin...
Real-world events like the COVID-19 pandemic and wildfires in Australia, Europe, and America remind us that the demands of complex operational settings are met by multiple, distributed teams interwoven with a large array of artefacts and networked te...
Artificial Intelligence (AI) is being increasingly implemented within road transport systems worldwide. Next generation of AI, Artificial General Intelligence (AGI) is imminent, and is anticipated to be more powerful than current AI. AGI systems will...
New developments in Artificial Intelligence (AI) are extensively discussed in public media and scholarly publications. While in many academic disciplines debates on the challenges and opportunities of Artificial Intelligence (AI) and how to best addr...
Artificial Intelligence (AI) holds great potential for the military domain but is also seen as prone to data bias and lacking transparency and explainability. In order to advance the trustworthiness of AI-enabled systems, a dynamic approach to the de...
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