Biochemical sensors have become indispensable tools for real-time, on-site monitoring and
analysis in diverse domains such as healthcare, environmental protection, and food safety …

Background Ensuring safe adoption of AI tools in healthcare hinges on access to sufficient
data for training, testing and validation. Synthetic data has been suggested in response to …

Objective The challenge of mixed-integer temporal data, which is particularly prominent for
medication use in the critically ill, limits the performance of predictive models. The purpose …

Abstract The Integrated Data Infrastructure (IDI) in New Zealand is a critical asset that
integrates citizen data from various public and private organizations for population-level …

In the urban landscaping field, training datasets for instance segmentation in the detection of
building facades are needed for complex analysis and simulation based on data. Manual …

Deep Learning is vital in medical imaging solutions and clinical applications. However,
multiple reasons, such as data scarcity and imbalance in the medical image dataset, cause …

Clinical research relies on high-quality patient data, however, obtaining big data sets is
costly and access to existing data is often hindered by privacy and regulatory concerns …

Knowledge distillation in neural networks refers to compressing a large model or dataset
into a smaller version of itself. We introduce Privacy Distillation, a framework that allows a …

Background and objective In early breast cancer diagnosis, tumor size is key to improving
the patient's survival chances. It helps doctors to determine the adequate treatment for each …

Data quality is a key factor in the development of trustworthy AI in healthcare. A large volume
of curated datasets with controlled confounding factors can improve the accuracy …